WorldWideScience

Sample records for digital maps

  1. Photogrammetry and Digital Mapping

    DEFF Research Database (Denmark)

    Frederiksen, Poul

    1998-01-01

    Technical tour to Lithuania, Poland and Estonia for 13 technical staff and managers of State Land Service, HQ, Latvia. Focus on technical aspects and management of geographical data for map production and administration. Visits to state and local government organisations and newly established...

  2. Sampling for validation of digital soil maps

    NARCIS (Netherlands)

    Brus, D.J.; Kempen, B.; Heuvelink, G.B.M.

    2011-01-01

    The increase in digital soil mapping around the world means that appropriate and efficient sampling strategies are needed for validation. Data used for calibrating a digital soil mapping model typically are non-random samples. In such a case we recommend collection of additional independent data and

  3. Digital soil mapping with limited data

    NARCIS (Netherlands)

    Hartemink, A.E.; McBratney, A.B.; Lourdes Mendonça-Santos, de M.

    2008-01-01

    There has been considerable expansion in the use of digital soil mapping technologies and development of methodologies that improve digital soil mapping at all scales and levels of resolution. These developments have occurred in all parts of the world in the past few years and also in countries

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, , USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  6. Photogrammetry, Digital mapping and Land Informations Systems

    DEFF Research Database (Denmark)

    Frederiksen, Poul

    1998-01-01

    Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia.......Monitoring activities on photogrammetry, digital mapping and land information systems in State Land Service in Latvia in relation to the EU Phare Project Phase II, Technical Assistance to land Privatisation and registration in Latvia....

  7. Digital technical map of Košice city

    Directory of Open Access Journals (Sweden)

    Štefan Kuzevič

    2007-06-01

    Full Text Available The digital map of a city is comprised complex of the map. Process of creation of the digital map of a city is time consuming and financially demanding. The digital map is created as co-operation of the local authority, technology network administrator, companies, and local government. The exact and current digital map of the city utilizable for multilateral applications is the result of this co-operation. The digital map of city catch all important phenomenon and objects which are needed for making decisions and planning to all grades controlling the local authority. The Geographic information systems tools make possible updating digital map, analyses and syntheses spatial phenomena and theirs relationships. The digital map of city is created step by step. The digital technical map of city is one of very important part of the digital map. Illustration of the part digital technical map of Košice is showed in the Fig. 1.

  8. Digital mapping in extreme and remote environments

    Science.gov (United States)

    Andersson, Joel; Bauer, Tobias; Sarlus, Zimer; Zainy, Maher; Brethes, Anais

    2017-04-01

    During the last few years, Luleå University of Technology has performed a series of research projects in remote areas with extreme climatic conditions using digital mapping technologies. The majority of past and ongoing research projects focus on the arctic regions of the Fennoscandian Shield and Greenland but also on the Zagros fold-and-thrust belt in northern Iraq. Currently, we use the Midland Valley application FieldMove on iPad mini devices with ruggedized casings. As all projects have a strong focus on geological field work, harsh climatic conditions are a challenge not only for the geologists but also for the digital mapping hardware. In the arctic regions especially cold temperatures affect battery lifetime and performance of the screens. But also high temperatures are restricting digital mapping. From experience, a typical temperature range where digital mapping, using iPad tablets, is possible lies between -20 and +40 degrees. Furthermore, the remote character of field areas complicates access but also availability of electricity. By a combination of robust solar chargers and ruggedized batteries we are able to work entirely autarkical. Additionally, we are currently installing a drone system that allows us to map outcrops normally inaccessible because of safety reasons or time deficiency. The produced data will subsequently be taken into our Virtual Reality studio for interpretation and processing. There we will be able to work also with high resolution DEM data from Lidar scanning allowing us to interpret structural features such as post-glacial faults in areas that are otherwise only accessible by helicopter. By combining digital field mapping with drone technique and a Virtual Reality studio we are able to work in hardly accessible areas, improve safety during field work and increase efficiency substantially.

  9. Inception Report, Photogrammetry and Digital Mapping, LATIVA

    DEFF Research Database (Denmark)

    Frederiksen, Poul

    1996-01-01

    The report gives the current situation on photogrammetry and digital mapping by the end of 1996 in Latvia.Objectives and proposals are given for activities of the EU Phare project: Technical Assistance to Land Privatisation and Registration in Latvia.The project is executed by Kampsax Geoplan...

  10. Digital mapping techniques '06 - Workshop proceedings

    Science.gov (United States)

    Soller, David R.

    2007-01-01

    The Digital Mapping Techniques `06 (DMT`06) workshop was attended by more than 110 technical experts from 51 agencies, universities, and private companies, including representatives from 27 state geological surveys (see Appendix A of these Proceedings). This workshop was similar in nature to the previous nine meetings, which were held in Lawrence, Kansas (Soller, 1997), Champaign, Illinois (Soller, 1998), Madison, Wisconsin (Soller, 1999), Lexington, Kentucky (Soller, 2000), Tuscaloosa, Alabama (Soller, 2001), Salt Lake City, Utah (Soller, 2002), Millersville, Pennsylvania (Soller, 2003), Portland, Oregon (Soller, 2004), and Baton Rouge, Louisiana (Soller, 2005). This year?s meeting was hosted by the Ohio Geological Survey, from June 11-14, 2006, on the Ohio State University campus in Columbus, Ohio. As in the previous meetings, the objective was to foster informal discussion and exchange of technical information. It is with great pleasure that I note that the objective was successfully met, as attendees continued to share and exchange knowledge and information, and renew friendships and collegial work begun at past DMT workshops.Each DMT workshop has been coordinated by the Association of American State Geologists (AASG) and U.S. Geological Survey (USGS) Data Capture Working Group, the latter of which was formed in August 1996 to support the AASG and the USGS in their effort to build a National Geologic Map Database (see Soller, this volume, and http://ngmdb.usgs.gov/info/standards/datacapt/). The Working Group was formed because increased production efficiencies, standardization, and quality of digital map products were needed for the database - and for the State and Federal geological surveys - to provide more high-quality digital maps to the public.At the 2006 meeting, oral and poster presentations and special discussion sessions emphasized: 1) methods for creating and publishing map products (here, "publishing" includes Web-based release); 2) field data

  11. County digital geologic mapping. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Hess, R.H.; Johnson, G.L.; dePolo, C.M.

    1995-12-31

    The purpose of this project is to create quality-county wide digital 1:250,000-scale geologic maps from existing published 1:250,000-scale Geologic and Mineral Resource Bulletins published by the Nevada Bureau of Mines and Geology (NBMG). An additional data set, based on current NBMG research, Major and Significant Quaternary and Suspected Quaternary Faults of Nevada, at 1:250,000 scale has also been included.

  12. County digital geologic mapping. Final report

    International Nuclear Information System (INIS)

    Hess, R.H.; Johnson, G.L.; dePolo, C.M.

    1995-01-01

    The purpose of this project is to create quality-county wide digital 1:250,000-scale geologic maps from existing published 1:250,000-scale Geologic and Mineral Resource Bulletins published by the Nevada Bureau of Mines and Geology (NBMG). An additional data set, based on current NBMG research, Major and Significant Quaternary and Suspected Quaternary Faults of Nevada, at 1:250,000 scale has also been included

  13. Digital Geological Mapping for Earth Science Students

    Science.gov (United States)

    England, Richard; Smith, Sally; Tate, Nick; Jordan, Colm

    2010-05-01

    This SPLINT (SPatial Literacy IN Teaching) supported project is developing pedagogies for the introduction of teaching of digital geological mapping to Earth Science students. Traditionally students are taught to make geological maps on a paper basemap with a notebook to record their observations. Learning to use a tablet pc with GIS based software for mapping and data recording requires emphasis on training staff and students in specific GIS and IT skills and beneficial adjustments to the way in which geological data is recorded in the field. A set of learning and teaching materials are under development to support this learning process. Following the release of the British Geological Survey's Sigma software we have been developing generic methodologies for the introduction of digital geological mapping to students that already have experience of mapping by traditional means. The teaching materials introduce the software to the students through a series of structured exercises. The students learn the operation of the software in the laboratory by entering existing observations, preferably data that they have collected. Through this the students benefit from being able to reflect on their previous work, consider how it might be improved and plan new work. Following this they begin fieldwork in small groups using both methods simultaneously. They are able to practise what they have learnt in the classroom and review the differences, advantages and disadvantages of the two methods, while adding to the work that has already been completed. Once the field exercises are completed students use the data that they have collected in the production of high quality map products and are introduced to the use of integrated digital databases which they learn to search and extract information from. The relatively recent development of the technologies which underpin digital mapping also means that many academic staff also require training before they are able to deliver the

  14. Securing Digital Audio using Complex Quadratic Map

    Science.gov (United States)

    Suryadi, MT; Satria Gunawan, Tjandra; Satria, Yudi

    2018-03-01

    In This digital era, exchanging data are common and easy to do, therefore it is vulnerable to be attacked and manipulated from unauthorized parties. One data type that is vulnerable to attack is digital audio. So, we need data securing method that is not vulnerable and fast. One of the methods that match all of those criteria is securing the data using chaos function. Chaos function that is used in this research is complex quadratic map (CQM). There are some parameter value that causing the key stream that is generated by CQM function to pass all 15 NIST test, this means that the key stream that is generated using this CQM is proven to be random. In addition, samples of encrypted digital sound when tested using goodness of fit test are proven to be uniform, so securing digital audio using this method is not vulnerable to frequency analysis attack. The key space is very huge about 8.1×l031 possible keys and the key sensitivity is very small about 10-10, therefore this method is also not vulnerable against brute-force attack. And finally, the processing speed for both encryption and decryption process on average about 450 times faster that its digital audio duration.

  15. Content Layer progressive Coding of Digital Maps

    DEFF Research Database (Denmark)

    Forchhammer, Søren; Jensen, Ole Riis

    2002-01-01

    A new lossless context based method is presented for content progressive coding of limited bits/pixel images, such as maps, company logos, etc., common on the World Wide Web. Progressive encoding is achieved by encoding the image in content layers based on color level or other predefined...... information. Information from already coded layers are used when coding subsequent layers. This approach is combined with efficient template based context bilevel coding, context collapsing methods for multilevel images and arithmetic coding. Relative pixel patterns are used to collapse contexts. Expressions...... for calculating the resulting number of contexts are given. The new methods outperform existing schemes coding digital maps and in addition provide progressive coding. Compared to the state-of-the-art PWC coder, the compressed size is reduced to 50-70% on our layered map test images....

  16. Content layer progressive coding of digital maps

    DEFF Research Database (Denmark)

    Forchhammer, Søren; Jensen, Ole Riis

    2000-01-01

    A new lossless context based method is presented for content progressive coding of limited bits/pixel images, such as maps, company logos, etc., common on the WWW. Progressive encoding is achieved by separating the image into content layers based on other predefined information. Information from...... already coded layers are used when coding subsequent layers. This approach is combined with efficient template based context bi-level coding, context collapsing methods for multi-level images and arithmetic coding. Relative pixel patterns are used to collapse contexts. The number of contexts are analyzed....... The new methods outperform existing coding schemes coding digital maps and in addition provide progressive coding. Compared to the state-of-the-art PWC coder, the compressed size is reduced to 60-70% on our layered test images....

  17. Digital compilation bedrock geologic map of the Warren quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG95-4A Walsh, GJ, Haydock, S, Prewitt, J, Kraus, J, Lapp, E, O'Loughlin, S, and Stanley, RS, 1995, Digital compilation bedrock geologic map of the...

  18. Digital bedrock geologic map of the Saxtons River quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG96-52A Ratcliffe, NM�and Armstrong, TR, 1996, Digital bedrock geologic map of the Saxtons River quadrangle, Vermont, USGS Open-File Report...

  19. Digital bedrock geologic map of the Cavendish quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG95-203A Ratcliffe, NM, 1995,�Digital bedrock geologic map of the Cavendish quadrangle, Vermont: USGS Open-File Report 95-203, 2 plates, scale...

  20. Digital compilation bedrock geologic map of the Milton quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG95-8A Dorsey, R, Doolan, B, Agnew, PC, Carter, CM, Rosencrantz, EJ, and Stanley, RS, 1995, Digital compilation bedrock geologic map of the Milton...

  1. Digital compilation bedrock geologic map of the Lincoln quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG95-5A Stanley, R, DelloRusso, V, Haydock, S, Lapp, E, O'Loughlin, S, Prewitt, J,and Tauvers, PR, 1995, Digital compilation bedrock geologic map...

  2. Mapping specific soil functions based on digital soil property maps

    Science.gov (United States)

    Pásztor, László; Fodor, Nándor; Farkas-Iványi, Kinga; Szabó, József; Bakacsi, Zsófia; Koós, Sándor

    2016-04-01

    Quantification of soil functions and services is a great challenge in itself even if the spatial relevance is supposed to be identified and regionalized. Proxies and indicators are widely used in ecosystem service mapping. Soil services could also be approximated by elementary soil features. One solution is the association of soil types with services as basic principle. Soil property maps however provide quantified spatial information, which could be utilized more versatilely for the spatial inference of soil functions and services. In the frame of the activities referred as "Digital, Optimized, Soil Related Maps and Information in Hungary" (DOSoReMI.hu) numerous soil property maps have been compiled so far with proper DSM techniques partly according to GSM.net specifications, partly by slightly or more strictly changing some of its predefined parameters (depth intervals, pixel size, property etc.). The elaborated maps have been further utilized, since even DOSoReMI.hu was intended to take steps toward the regionalization of higher level soil information (secondary properties, functions, services). In the meantime the recently started AGRAGIS project requested spatial soil related information in order to estimate agri-environmental related impacts of climate change and support the associated vulnerability assessment. One of the most vulnerable services of soils in the context of climate change is their provisioning service. In our work it was approximated by productivity, which was estimated by a sequential scenario based crop modelling. It took into consideration long term (50 years) time series of both measured and predicted climatic parameters as well as accounted for the potential differences in agricultural practice and crop production. The flexible parametrization and multiple results of modelling was then applied for the spatial assessment of sensitivity, vulnerability, exposure and adaptive capacity of soils in the context of the forecasted changes in

  3. Using Digital Mapping Tool in Ill-Structured Problem Solving

    Science.gov (United States)

    Bai, Hua

    2013-01-01

    Scaffolding students' problem solving and helping them to improve problem solving skills are critical in instructional design courses. This study investigated the effects of students' uses of a digital mapping tool on their problem solving performance in a design case study. It was found that the students who used the digital mapping tool…

  4. Computer aided site management. Site use management by digital mapping

    International Nuclear Information System (INIS)

    Chupin, J.C.

    1990-01-01

    The logistics program developed for assisting the Hague site management is presented. A digital site mapping representation and geographical data bases are used. The digital site map and its integration into a data base are described. The program can be applied to urban and rural land management aid. Technical administrative and economic evaluations of the program are summarized [fr

  5. The genotype-phenotype map of an evolving digital organism

    OpenAIRE

    Fortuna, Miguel A.; Zaman, Luis; Ofria, Charles; Wagner, Andreas

    2017-01-01

    To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms fr...

  6. Digital geologic map in the scale 1:50 000

    International Nuclear Information System (INIS)

    Kacer, S.; Antalik, M.

    2005-01-01

    In this presentation authors present preparation of new digital geologic map of the Slovak Republic. This map is prepared by the State Geological Institute of Dionyz Stur as a part of the project Geological information system GeoIS. One of the basic information geologic layers, which will be accessible on the web-site will be digital geologic map of the Slovak Republic in the scale 1: 50 000

  7. Spatial Digital Database for the Geologic Map of Oregon

    Science.gov (United States)

    Walker, George W.; MacLeod, Norman S.; Miller, Robert J.; Raines, Gary L.; Connors, Katherine A.

    2003-01-01

    Introduction This report describes and makes available a geologic digital spatial database (orgeo) representing the geologic map of Oregon (Walker and MacLeod, 1991). The original paper publication was printed as a single map sheet at a scale of 1:500,000, accompanied by a second sheet containing map unit descriptions and ancillary data. A digital version of the Walker and MacLeod (1991) map was included in Raines and others (1996). The dataset provided by this open-file report supersedes the earlier published digital version (Raines and others, 1996). This digital spatial database is one of many being created by the U.S. Geological Survey as an ongoing effort to provide geologic information for use in spatial analysis in a geographic information system (GIS). This database can be queried in many ways to produce a variety of geologic maps. This database is not meant to be used or displayed at any scale larger than 1:500,000 (for example, 1:100,000). This report describes the methods used to convert the geologic map data into a digital format, describes the ArcInfo GIS file structures and relationships, and explains how to download the digital files from the U.S. Geological Survey public access World Wide Web site on the Internet. Scanned images of the printed map (Walker and MacLeod, 1991), their correlation of map units, and their explanation of map symbols are also available for download.

  8. Dynamic Digital Maps as Vehicles for Distributing Digital Geologic Maps and Embedded Analytical Data and Multimedia

    Science.gov (United States)

    Condit, C. D.; Mninch, M.

    2012-12-01

    The Dynamic Digital Map (DDM) is an ideal vehicle for the professional geologist to use to describe the geologic setting of key sites to the public in a format that integrates and presents maps and associated analytical data and multimedia without the need for an ArcGIS interface. Maps with field trip guide stops that include photographs, movies and figures and animations, showing, for example, how the features seen in the field formed, or how data might be best visualized in "time-frame" sequences are ideally included in DDMs. DDMs distribute geologic maps, images, movies, analytical data, and text such as field guides, in an integrated cross-platform, web enabled format that are intuitive to use, easily and quickly searchable, and require no additional proprietary software to operate. Maps, photos, movies and animations are stored outside the program, which acts as an organizational framework and index to present these data. Once created, the DDM can be downloaded from the web site hosting it in the flavor matching the user's operating system (e.g. Linux, Windows and Macintosh) as zip, dmg or tar files (and soon as iOS and Android tablet apps). When decompressed, the DDM can then access its associated data directly from that site with no browser needed. Alternatively, the entire package can be distributed and used from CD, DVD, or flash-memory storage. The intent of this presentation is to introduce the variety of geology that can be accessed from the over 25 DDMs created to date, concentrating on the DDM of the Springerville Volcanic Field. We will highlight selected features of some of them, introduce a simplified interface to the original DDM (that we renamed DDMC for Classic) and give a brief look at a the recently (2010-2011) completed geologic maps of the Springerville Volcanic field to see examples of each of the features discussed above, and a display of the integrated analytical data set. We will also highlight the differences between the classic or

  9. Digital Mapping and Land Information Systems - Volume 6

    DEFF Research Database (Denmark)

    Frederiksen, Poul

    1998-01-01

    Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ. Develop......Introduction of digital mapping techniques in the 28 counties of Latvia related to the offices of the national mapping agency (State Land Service). Major components are: Training of regional staff, procurement of hard- and software, training of technical staff from State Land Service, HQ...

  10. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, SC

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. Digital Flood Insurance Rate Map Database, Buchanan County, Iowa, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLEMING COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Charles COUNTY, MD, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTLER COUNTY, NE

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CITY OF SACRAMENTO, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HONOLULU COUNTY, HI, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KAUAI COUNTY, HAWAII, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SARPY COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUCAS COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NASSAU COUNTY, NEW YORK

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARDIN COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, NEW YORK

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. Digital Flood Insurance Rate Map Database, PRINCE GEORGE, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, OHIO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAGONER COUNTY, OKLAHOMA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, MASSACHUSETTS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAIRFIELD COUNTY, CONNECTICUT

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE ROCKLAND COUNTY, NY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, OHIO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. Radarsat Antarctic Mapping Project Digital Elevation Model, Version 2

    Data.gov (United States)

    National Aeronautics and Space Administration — The high-resolution Radarsat Antarctic Mapping Project (RAMP) Digital Elevation Model (DEM) combines topographic data from a variety of sources to provide consistent...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAUNDERS COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RED WILLOW COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, KANSAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARLAN COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RENO COUNTY, KANSAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE FOR HOWARD COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FURNAS COUNTY, NEBRASKA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, NE, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLAMAKEE COUNTY, IOWA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CASS COUNTY, NEBRASKA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCPHERSON COUNTY, KANSAS

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAWES COUNTY, NEBRASKA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALLEY COUNTY, NEBRASKA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLSWORTH COUNTY, KANSAS

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERMAN COUNTY, NEBRASKA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARVEY COUNTY, KANSAS

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLATTE COUNTY, NEBRASKA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, NEBRASKA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, THURSTON COUNTY, NEBRASKA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAMA COUNTY, IOWA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, NEBRASKA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Eddy County, NM

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RAY COUNTY, MISSOURI, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, IDAHO, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAWRENCE COUNTY, OHIO, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, WISCONSIN, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, AR

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEBASTIAN COUNTY, ARKANSAS

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FERGUS COUNTY, MONTANA, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BONNER COUNTY, IDAHO

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JOAQUIN COUNTY, CALIFORNIA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LORAIN COUNTY, OHIO USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROBERTSON COUNTY, KY

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COSHOCTON COUNTY, OHIO, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, TX

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, OHIO, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRANT COUNTY, KY

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESSEX COUNTY, MA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARROLL COUNTY, GEORGIA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, ILLINOIS USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas County, Oregon, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINCOLN COUNTY, ARKANSAS, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POWELL COUNTY, KY

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COLFAX COUNTY, New Mexico

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Winston COUNTY, AL

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CONVERSE COUNTY, WYOMING, USA.

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Dougherty County, GA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, TX

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HUNTERDON CO., NJ

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEDGWICK COUNTY, KANSAS, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FINNEY COUNTY, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CULLMAN COUNTY, AL

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, GEORGIA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Northumberland County, VA, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CADDO PARISH, LOUISIANA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ETOWAH COUNTY, AL

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FORT BEND COUNTY, TEXAS

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LACLEDE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLINTON COUNTY, MISSOURI, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCDONALD COUNTY, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MERCER COUNTY, KY

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, AUGUSTA COUNTY, VA, USA

    Data.gov (United States)

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  16. Digital Flood Insurance Rate Map Database, Crawford County, PA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, OH, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, KY

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Spartanburg County, South Carolina

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Douglas COUNTY, Nevada

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Rio Grande County, Colorado

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Elbert County, Colorado

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JASPER COUNTY, TX

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Roosevelt COUNTY, New Mexico

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Linn County, Oregon, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wythe County, VA, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SULLIVAN COUNTY, PA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cleburne COUNTY, AL

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KITSAP COUNTY, WASHINGTON, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,CAMDEN COUNTY, GEORGIA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MENDOCINO COUNTY, CALIFORNIA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, APPLING COUNTY, GEORGIA, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Wilcox COUNTY, AL

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASHINGTON COUNTY, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALLARD COUNTY, KY

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, KY

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PASCO COUNTY, FLORIDA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GILMER COUNTY, GEORGIA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIAMI - DADE COUNTY, FLORIDA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, TX

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, AL

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALKER COUNTY, GEORGIA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAVIESS COUNTY, KY

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONROE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, GEORGIA, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARLBORO COUNTY, SC

    Data.gov (United States)

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  9. FINAL DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENWOOD COUNTY, SC

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RICE COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, KY

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHRISTIAN COUNTY, ILLINOIS USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, IA, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POTTAWATTAMIE COUNTY, IOWA, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MITCHELL COUNTY, IOWA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLAYTON COUNTY, IOWA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOWARD COUNTY, IOWA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABAES, LA PAZ COUNTY, AZ

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEWARD COUNTY, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SCOTT COUNTY, KY

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWTON COUNTY, GEORGIA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX, VA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KARNES COUNTY, TEXAS, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VOLUSIA COUNTY, FL, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Marshall COUNTY, AL

    Data.gov (United States)

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  8. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HEMPSTEAD COUNTY, AR

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TOM GREEN COUNTY, TEXAS

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Berks County, Pennsylvania, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STONE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VAL VERDE COUNTY, TEXAS

    Data.gov (United States)

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  13. Digital Flood Insurance Rate Map Database, Richmond County, Virginia, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WASATCH COUNTY, UTAH, USA

    Data.gov (United States)

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  15. Digital Flood Insurance Rate Map Database, Westmoreland County, Virginia, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, KENTUCKY, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCCRACKEN COUNTY, KY

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,GRAVES COUNTY, KY

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYON COUNTY, KY

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WOLFE COUNTY, KY

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEBSTER COUNTY, KY

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAWAII COUNTY, HAWAII, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCINTOSH COUNTY, GEORGIA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HART COUNTY, KY

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OSCEOLA COUNTY, FL

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ELLIOTT COUNTY, KY

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GAGE COUNTY, NEBRASKA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARK COUNTY, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, INDIANA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ANGELINA COUNTY, TX

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, NEBRASKA, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWPORT COUNTY, RHODE ISLAND

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ATASCOSA COUNTY, TEXAS

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DOUGLAS COUNTY, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, POLK COUNTY, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, KY

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEBANON COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CRAWFORD COUNTY, AR ,USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SOLANO COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Mitchell County, GA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAYLOR COUNTY, FL, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRIMES COUNTY, TX

    Data.gov (United States)

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  3. Digital Flood Insurance Rate Map Database, Allegheny County, Pennsylvania, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. LOUIS, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELAWARE COUNTY, OK, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, UNION COUNTY, FLORIDA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEE COUNTY, FLORIDA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HAMILTON COUNTY, FLORIDA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WAYNE COUNTY, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Jefferson COUNTY, AL

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALTON COUNTY, FL, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Kenton COUNTY, Kentucky

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSSELL COUNTY, KY

    Data.gov (United States)

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  14. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LANCASTER COUNTY, SC

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHAMBERS COUNTY, TEXAS

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHATHAM COUNTY, GEORGIA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HILLSBOROUGH COUNTY, FLORIDA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALLER COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CARBON COUNTY, UTAH

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Liberty County, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SAN JACINTO COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, New London County, CT

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Westmoreland County, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. Digital Flood Insurance Rate Map Database, Sussex County, Delaware, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TYLER COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Stafford County , VIRGINIA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENVILLE COUNTY, SOUTH CAROLINA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DICKENSON COUNTY, VA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, TX

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DESOTO COUNTY, FL, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FANNIN COUNTY, GEORGIA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST. FRANCOIS COUNTY, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAFAYETTE COUNTY, MISSOURI, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDALL COUNTY, TX, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PEMBINA COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCLEAN COUNTY, KY

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RUSK COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Nelson County, VA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHEROKEE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NELSON COUNTY, KY

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDDLESEX COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ESCAMBIA COUNTY, AL

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Baldwin COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BARTOW COUNTY, GEORGIA, USA

    Data.gov (United States)

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  6. A Visual Framework for Digital Reconstruction of Topographic Maps

    KAUST Repository

    Thabet, Ali Kassem; Smith, Neil; Wittmann, Roland; Schneider, Jens

    2014-01-01

    , this method has broad applicability for digitization and reconstruction of the world's old topographic maps that are often the only record of past landscapess and cultural heritage before their destruction under modern development.

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALPENA COUNTY, MI

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALDWELL PARISH, LOUISIANA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHIAWASSEE COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  10. Digital Flood Insurance Rate Map for Vermillion County, IN

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EL DORADO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GADSDEN COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, FLORIDA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHELBY COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Harris COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIKE COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. Digital Flood Insurance Rate Map Database, Bradford County, Pennsylvania, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JOHNSON COUNTY, KY

    Data.gov (United States)

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  19. Digital Flood Insurance Rate Map Database, Charles County, Maryland, USA

    Data.gov (United States)

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  20. Digital Flood Insurance Rate Map Database, Essex County, Virginia, USA

    Data.gov (United States)

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  1. Digital Flood Insurance Rate Map Database, Calvert County, Maryland, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COBB COUNTY, GA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MATHEWS COUNTY, VIRGINIA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, OHIO

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOLMES COUNTY, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KOOTENAI COUNTY, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Accomack County, VIRGINIA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARKE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LYCOMING COUNTY, PENNSYLVANIA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WHATCOM COUNTY, WASHINGTON

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HARRISON COUNTY, TEXAS

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DELTA COUNTY, COLORADO, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GULF COUNTY, FLORIDA, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Delaware County, Pennsylvania, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HALL COUNTY, NE, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NORFOLK COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COWLEY COUNTY, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TALBOT, MARYLAND, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PATRICK COUNTY, VA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RANDOLPH COUNTY, WV, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAYSON COUNTY, VA, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTERFIELD, VA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SURRY COUNTY, VA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MASSACHUSETTS

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Buckingham County, VA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GARRETT COUNTY, Maryland, USA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, OTTAWA COUNTY, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALEIGH COUNTY, WV, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Essex County, VA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAND COUNTY, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Caroline COUNTY, Maryland, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TUCKER COUNTY, WV, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Sussex County, VA, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WESTMORELAND COUNTY, VA, USA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FLUVANNA COUNTY, VA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Richmond County, VA, USA

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Pulaski County, VA, USA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Scott County, VA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DILLON COUNTY, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Upshur County, WV, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALLEN COUNTY, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LINN COUNTY, IA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAROLINE COUNTY, VA, USA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BANDERA COUNTY, TEXAS

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Washington COUNTY, NE

    Data.gov (United States)

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  7. Digital Flood Insurance Rate Map Database, Mercer County, PA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ST JOSEPH COUNTY, MI

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHERBURNE COUNTY, MINNESOTA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DUKES COUNTY, MA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Terrell County, GA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GLOUCESTER, VA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WORCESTER COUNTY, MA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SONOMA COUNTY, CALIFORNIA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MONMOUTH COUNTY, NEW JERSEY

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YORK COUNTY, PA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BENTON COUNTY, ARKANSAS

    Data.gov (United States)

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  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GORDON COUNTY, GEORGIA

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUSSEX COUNTY, NEW JERSEY

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARIN COUNTY, CALIFORNIA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLEARFIELD COUNTY, PA, USA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COOPER COUNTY, MISSOURI, USA

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAMERON COUNTY, PA, USA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BOYLE COUNTY, KY

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WARREN COUNTY, NEW JERSEY

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FAYETTE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUFFOLK COUNTY, NEW YORK

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bell COUNTY, Kentucky

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BATH COUNTY, VIRGINIA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, INDIAN RIVER COUNTY, FL

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SIMPSON COUNTY, KY

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Cherokee COUNTY, AL

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, AL

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Lancaster County, VA, USA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BEDFORD COUNTY, VA, USA

    Data.gov (United States)

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  16. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HONOLULU COUNTY, HI

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MAYES COUNTY, OK

    Data.gov (United States)

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  18. DRAFT DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NEWBERRY COUNTY, SC

    Data.gov (United States)

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  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GREENE COUNTY, GEORGIA, USA

    Data.gov (United States)

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  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FRANKLIN COUNTY, VA, USA

    Data.gov (United States)

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  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HIGHLAND COUNTY, VIRGINIA

    Data.gov (United States)

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  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMTER COUNTY, AL

    Data.gov (United States)

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  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRADY COUNTY, OKLAHOMA

    Data.gov (United States)

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  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SHASTA COUNTY, CALIFORNIA

    Data.gov (United States)

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  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOWELL COUNTY, MISSOURI, USA

    Data.gov (United States)

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  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SISKIYOU COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YOLO COUNTY, CALIFORNIA

    Data.gov (United States)

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  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PLUMAS COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ORANGE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RIVERSIDE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

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  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Tuolumne County, USA

    Data.gov (United States)

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  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PIMA COUNTY, ARIZONA, USA

    Data.gov (United States)

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  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COCHISE COUNTY, ARIZONA, USA

    Data.gov (United States)

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  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MADERA COUNTY, CALIFORNIA

    Data.gov (United States)

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  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TEHAMA COUNTY, CALIFORNIA

    Data.gov (United States)

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  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, YUMA COUNTY, ARIZONA, USA

    Data.gov (United States)

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  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SACRAMENTO COUNTY, CALIFORNIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BUTTE COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLATSOP COUNTY, OR, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HENRY COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RALLS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOUSTON COUNTY, Georgia

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MIDLAND COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HOPKINS COUNTY, KY

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EATON COUNTY, MICHIGAN, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Oswego COUNTY, New York

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Butts County, GA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BULLOCH COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  10. DIGITAL FLOOD INSURACE RATE MAP DATABASE, LEON COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JONES COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LUNA COUNTY, New Mexico

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROSS COUNTY, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CALHOUN COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BALTIMORE CITY, MARYLAND

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Bucks COUNTY, PA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. Digital Subtraction Angiography (DSA) "Road Map": An Angiographic Tool

    Science.gov (United States)

    Turski, P. A.; Stieghorst, M. F.; Strother, C. M.; Crummy, A. B.; Lieberman, R. P.; Mistretta, C. A.

    1982-12-01

    Continuous Digital subtraction combined with intraarterial injections of contrast medium permits the display of arterial structures during real time fluoroscopy. This DSA "road map" facilitates selective catheterization and has proved useful in interventional procedures.

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Chambers COUNTY, AL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WALTON COUNTY, GEORGIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HURON COUNTY, MICHIGAN USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEVY COUNTY, FL, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ROGERS COUNTY, OKLAHOMA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEDINA COUNTY, TX

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. Digital Flood Insurance Rate Map Database, Middlesex County, Virginia, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LAKE COUNTY, ILLINOIS USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, STARK COUNTY, OHIO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DAUPHIN COUNTY, PENNSYLVANIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBEMARLE COUNTY, VIRGINIA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, City of Poquoson, Virginia

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. Digital bedrock geologic map of the Andover quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG96-31A Ratcliffe, N.M., 1996,�Digital bedrock geologic map of the Andover quadrangle, Vermont: USGS Open-File Report 96-31-A, 2 plates, scale...

  11. Digital Soil Mapping – A platform for enhancing soil learning.

    Science.gov (United States)

    The expansion of digital infrastructure and tools has generated massive data and information as well as a need for reliable processing and accurate interpretations. Digital Soil Mapping is no exception in that it has provided opportunities for professionals and the public to interact at field and tr...

  12. Digital bedrock geologic map of the Weston quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG96-526A Ratcliffe, NM�and Burton, WC, 1996,�Digital bedrock geologic map of the Weston quadrangle, Vermont: USGS Open-File Report 96-526, 2...

  13. Digital bedrock geologic map of the Chester quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG95-576A Ratcliffe, N.M., 1995,�Digital bedrock geologic map of the Chester quadrangle, Vermont: USGS Open-File Report 95-576, 2 plates, scale...

  14. Digital bedrock geologic map of the Plymouth quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG94-654A Walsh, G.J., and Ratcliffe, N.M., 1994,�Digital bedrock geologic map of the Plymouth quadrangle, Vermont: USGS Open-File Report 94-654, 2...

  15. Digital bedrock geologic map of the Johnson quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG98-2 Thompson, PJ�and Thompson, TB, 1998,�Digital bedrock geologic map of the Johnson quadrangle, Vermont: VGS Open-File Report VG98-2, 2 plates,...

  16. Digital bedrock geologic map of the Rochester quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG96-33A Walsh, GJ�and Falta, CK, 1996, Digital bedrock geologic map of the Rochester quadrangle, Vermont: USGS Open-File Report 96-33-A, 2 plates,...

  17. Digital bedrock geologic map of the Eden quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG98-3 Kim, J, Springston, G, and Gale, M, 1998,�Digital bedrock geologic map of the Eden quadrangle, Vermont: VGS Open-File Report VG98-3, 2...

  18. USGS QA Plan: Certification of digital airborne mapping products

    Science.gov (United States)

    Christopherson, J.

    2007-01-01

    To facilitate acceptance of new digital technologies in aerial imaging and mapping, the US Geological Survey (USGS) and its partners have launched a Quality Assurance (QA) Plan for Digital Aerial Imagery. This should provide a foundation for the quality of digital aerial imagery and products. It introduces broader considerations regarding processes employed by aerial flyers in collecting, processing and delivering data, and provides training and information for US producers and users alike.

  19. Digital Geologic Map of New Mexico - Formations

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — The geologic map was created in GSMAP at Socorro, New Mexico by Orin Anderson and Glen Jones and published as the Geologic Map of New Mexico 1:500,000 in GSMAP...

  20. Why Map Issues? On Controversy Analysis as a Digital Method.

    Science.gov (United States)

    Marres, Noortje

    2015-09-01

    This article takes stock of recent efforts to implement controversy analysis as a digital method in the study of science, technology, and society (STS) and beyond and outlines a distinctive approach to address the problem of digital bias. Digital media technologies exert significant influence on the enactment of controversy in online settings, and this risks undermining the substantive focus of controversy analysis conducted by digital means. To address this problem, I propose a shift in thematic focus from controversy analysis to issue mapping. The article begins by distinguishing between three broad frameworks that currently guide the development of controversy analysis as a digital method, namely, demarcationist, discursive, and empiricist. Each has been adopted in STS, but only the last one offers a digital "move beyond impartiality." I demonstrate this approach by analyzing issues of Internet governance with the aid of the social media platform Twitter.

  1. Creation of a Cell-Based Digital Cadastral Mapping System (Digital ...

    African Journals Online (AJOL)

    Digital cadastre enhances land transaction activities to be conducted in a business manner. Similarly, land subdivision or boundary redefinition, land registration and land marketing are achieved with better accuracy. This paper discusses the need to introduce a national Cell-Based Digital Cadastral Mapping System model ...

  2. State Base Map for GIS – New Digital Topographic Map of the Republic of Macedonia

    Directory of Open Access Journals (Sweden)

    Zlatko Srbinoski

    2009-12-01

    Full Text Available The basic aim of the National Spatial Data Infrastructure (NSDI built in accordance with INSPIRE directive is to standardize spatial data infrastructure on national level. In that direction, topographic maps are a basic platform for acquiring spatial data within geoinformation systems and one of the most important  segments of NSDI. This paper presents methodology of establishing the new digital topographic map of the Republic of Macedonia titled “State Base Map for GIS in Macedonia”. This paper analyzes geometrical accuracy of new digital topographic maps. Production of the new digital topographic map has been the most important cartographic project in the Republic of Macedonia since it became independent.

  3. New modified map for digital image encryption and its performance

    Science.gov (United States)

    Suryadi, MT; Yus Trinity Irsan, Maria; Satria, Yudi

    2017-10-01

    Protection to classified digital data becomes so important in avoiding data manipulation and alteration. The focus of this paper is in data and information protection of digital images form. Protection is provided in the form of encrypted digital image. The encryption process uses a new map, {x}n+1=\\frac{rλ {x}n}{1+λ {(1-{x}n)}2}\\quad ({mod} 1), which is called MS map. This paper will show: the results of digital image encryption using MS map and how the performance is regarding the average time needed for encryption/decryption process; randomness of key stream sequence with NIST test, histogram analysis and goodness of fit test, quality of the decrypted image by PSNR, initial value sensitivity level, and key space. The results show that the average time of the encryption process is relatively same as the decryption process and it depends to types and sizes of the image. Cipherimage (encrypted image) is uniformly distributed since: it passes the goodness of fit test and also the histogram of the cipherimage is flat; key stream, that are generated by MS map, passes frequency (monobit) test, and runs test, which means the key stream is a random sequence; the decrypted image has same quality as the original image; and initial value sensitivity reaches 10-17, and key space reaches 3.24 × 10634. So, that encryption algorithm generated by MS map is more resistant to brute-force attack and known plaintext attack.

  4. Aircraft route planning based on digital map pre-treatment

    Directory of Open Access Journals (Sweden)

    Ran ZHEN

    2015-04-01

    Full Text Available Aiming at the flight path project in low complicated airspace, the influence of terrain conditions and surface threatening to aircraft flight are studied. Through the analysis of digital map and static threat, the paper explores the processing method of the digital map, and uses the Hermite function to process the map smoothly, reducing the searching range of optimal trajectory. By designing the terrain following, terrain avoidance and the way of avoiding a threat, the safety of aircraft can be guaranteed. In-depth analysis of particle swarm optimization (PSO algorithm realizes the three dimensional paths project before the aircraft performs a task. Through simulation, the difference of the maps before and after processing is shown, and offline programming of the three dimensional optimal path is achieved.

  5. Small forest cuttings mapped with Landsat digital data

    Science.gov (United States)

    Bryant, E.; Dodge, A. G.; Eger, M. J. E.

    1979-01-01

    The Cooperative Landsat Applications Research Group used computer classification of Landsat digital data to map forest cuttings (clearcuts) in northern New Hampshire. Cuttings as small as 3 hectares were identified. Several ages or conditions of clearcuts could be distinguished. Progress in two methods of duplicating classification categories from one Landsat pass to another are discussed. One method was used in making maps of areas in 1973, 1975, and 1978.

  6. Digital Mapping Techniques '11–12 workshop proceedings

    Science.gov (United States)

    Soller, David R.

    2014-01-01

    The Digital Mapping Techniques '11 (DMT'11) workshop was hosted by Virginia Division of Geology and Mineral Resources and The College of William & Mary, and coordinated by the National Geologic Map Database project. Conducted May 22-25 on the campus of The College of William & Mary, in Williamsburg, Virginia, it was attended by 77 technical experts from 30 agencies, universities, and private companies, including representatives from 19 State geological surveys (see "DMT'11 Presentations and Attendees" in these Proceedings).

  7. Small scale digital soil mapping in Southeastern Kenya

    NARCIS (Netherlands)

    Mora Vallejo, A.P.; Claessens, L.; Stoorvogel, J.J.; Heuvelink, G.B.M.

    2008-01-01

    Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether

  8. Digital soil mapping: strategy for data pre-processing

    Directory of Open Access Journals (Sweden)

    Alexandre ten Caten

    2012-08-01

    Full Text Available The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM. Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

  9. Data Sprints: A Collaborative Format in Digital Controversy Mapping

    DEFF Research Database (Denmark)

    Munk, Anders Kristian; Tommaso, Venturini; Meunier, Axel

    2017-01-01

    driven by a desire to provide navigational aids to actors faced with the challenge of making sense of complicated techno-scientific problems. Natively digital media technologies have thus been re-appropriated by STS researchers specifically for the purpose of mapping controversies in a way that would...... experiences with various forms of public engagement and participation. Through a concrete reappropriation of a collaborative format that is indeed native to the digital domain - namely the hackathon - we will show how digital methods can make a difference in participatory STS research. The data sprint, as we...... in amsterdam. Through a mix of digital methods ranging from web cartography and text mining to scientometrics and social media analysis we took on questions related to climate adaptation funding, vulnerability assessment, project management, and dynamics of the international negotiations. The sprints hardwired...

  10. Functional digital soil mapping for the prediction of available water capacity in Nigeria using legacy data

    NARCIS (Netherlands)

    Ugbaje, S.U.; Reuter, H.I.

    2013-01-01

    Soil information, particularly water storage capacity, is of utmost importance for assessing and managing land resources for sustainable land management. We investigated using digital soil mapping (DSM) and digital soil functional mapping (DSFM) procedures to predict available water capacity (AWC)

  11. A Visual Framework for Digital Reconstruction of Topographic Maps

    KAUST Repository

    Thabet, Ali Kassem

    2014-09-30

    We present a framework for reconstructing Digital Elevation Maps (DEM) from scanned topographic maps. We first rectify the images to ensure that maps fit together without distortion. To segment iso-contours, we have developed a novel semi-automated method based on mean-shifts that requires only minimal user interaction. Contour labels are automatically read using an OCR module. To reconstruct the output DEM from scattered data, we generalize natural neighbor interpolation to handle the transfinite case (contours and points). To this end, we use parallel vector propagation to compute a discrete Voronoi diagram of the constraints, and a modified floodfill to compute virtual Voronoi tiles. Our framework is able to handle tens of thousands of contours and points and can generate DEMs comprising more than 100 million samples. We provide quantitative comparison to commercial software and show the benefits of our approach. We furthermore show the robustness of our method on a massive set of old maps predating satellite acquisition. Compared to other methods, our framework is able to accurately and efficiently generate a final DEM despite inconsistencies, sparse or missing contours even for highly complex and cluttered maps. Therefore, this method has broad applicability for digitization and reconstruction of the world\\'s old topographic maps that are often the only record of past landscapess and cultural heritage before their destruction under modern development.

  12. Digital Soil Mapping Using Landscape Stratification for Arid Rangelands in the Eastern Great Basin, Central Utah

    OpenAIRE

    Fonnesbeck, Brook B.

    2015-01-01

    Digital soil mapping typically involves inputs of digital elevation models, remotely sensed imagery, and other spatially explicit digital data as environmental covariates to predict soil classes and attributes over a landscape using statistical models. Digital imagery from Landsat 5, a digital elevation model, and a digital geology map were used as environmental covariates in a 67,000-ha study area of the Great Basin west of Fillmore, UT. A “pre-map” was created for selecting sampling locatio...

  13. Using Digital Concept Maps to Distinguish Between Young Refugees’ Challenges

    OpenAIRE

    Brooker, Abi; Lawrence, Jeanette; Dodds, Agnes

    2017-01-01

    Digital media are beneficial for research of complex refugee issues, as they allow refugees to express their personal experiences of complex issues in ways that are not restricted by language barriers or limited in authenticity, while also offering researchers a way to systematically compare refugees’ varied experiences. We used a computerised concept mapping task to ask 74 young refugees (teenagers and young adults), from three separately recruited samples, to think about their experiences w...

  14. Classifying and mapping wetlands and peat resources using digital cartography

    Science.gov (United States)

    Cameron, Cornelia C.; Emery, David A.

    1992-01-01

    Digital cartography allows the portrayal of spatial associations among diverse data types and is ideally suited for land use and resource analysis. We have developed methodology that uses digital cartography for the classification of wetlands and their associated peat resources and applied it to a 1:24 000 scale map area in New Hampshire. Classifying and mapping wetlands involves integrating the spatial distribution of wetlands types with depth variations in associated peat quality and character. A hierarchically structured classification that integrates the spatial distribution of variations in (1) vegetation, (2) soil type, (3) hydrology, (4) geologic aspects, and (5) peat characteristics has been developed and can be used to build digital cartographic files for resource and land use analysis. The first three parameters are the bases used by the National Wetlands Inventory to classify wetlands and deepwater habitats of the United States. The fourth parameter, geological aspects, includes slope, relief, depth of wetland (from surface to underlying rock or substrate), wetland stratigraphy, and the type and structure of solid and unconsolidated rock surrounding and underlying the wetland. The fifth parameter, peat characteristics, includes the subsurface variation in ash, acidity, moisture, heating value (Btu), sulfur content, and other chemical properties as shown in specimens obtained from core holes. These parameters can be shown as a series of map data overlays with tables that can be integrated for resource or land use analysis.

  15. BGS·SIGMA - Digital mapping at the British Geological Survey

    Science.gov (United States)

    Smith, Nichola; Lawrie, Ken

    2017-04-01

    Geological mapping methods have evolved significantly over recent decades and this has included the transition to digital field data capture. BGS has been developing methodologies and technologies for this since 2001, and has now reached a stage where our custom built data capture and map compilation system (BGS·SIGMAv2015) is the default toolkit, within BGS, for bedrock and superficial mapping across the UK and overseas. In addition, BGS scientists also use the system for other data acquisition projects, such as landslide assessment, geodiversity audits and building stone studies. BGS·SIGMAv2015 is an integrated toolkit which enables assembly, interrogation and visualisation of existing geological information; capture of, and integration with, new data and geological interpretations; and delivery of digital products and services. From its early days as a system which used PocketGIS run on Husky Fex21 hardware, to the present day system, developed using ESRI's ArcGIS built on top of a bespoke relational data model, running on ruggedized tablet PCs with integrated GPS units, the system has evolved into a comprehensive system for digital geological data capture, mapping and compilation. The benefits, for BGS, of digital data capture are huge. Not only are the data being gathered in a standardised format, with the use of dictionaries to ensure consistency, but project teams can start building their digital geological map in the field by merging data collected by colleagues, building line-work and polygons, and subsequently identifying areas for further investigation. This digital data can then be easily incorporated into corporate databases and used in 3D modelling and visualisation software once back in the office. BGS is now at a stage where the free external release of our digital mapping system is in demand across the world, with 3000 licences being issued to date, and is successfully being used by other geological surveys, universities and exploration companies

  16. Digital Field Mapping with the British Geological Survey

    Science.gov (United States)

    Leslie, Graham; Smith, Nichola; Jordan, Colm

    2014-05-01

    The BGS•SIGMA project was initiated in 2001 in response to a major stakeholder review of onshore mapping within the British Geological Survey (BGS). That review proposed a significant change for BGS with the recommendation that digital methods should be implemented for field mapping and data compilation. The BGS•SIGMA project (System for Integrated Geoscience MApping) is an integrated workflow for geoscientific surveying and visualisation using digital methods for geological data visualisation, recording and interpretation, in both 2D and 3D. The project has defined and documented an underpinning framework of best practice for survey and information management, best practice that has then informed the design brief and specification for a toolkit to support this new methodology. The project has now delivered BGS•SIGMA2012. BGS•SIGMA2012 is a integrated toolkit which enables assembly and interrogation/visualisation of existing geological information; capture of, and integration with, new data and geological interpretations; and delivery of 3D digital products and services. From its early days as a system which used PocketGIS run on Husky Fex21 hardware, to the present day system which runs on ruggedized tablet PCs with integrated GPS units, the system has evolved into a complete digital mapping and compilation system. BGS•SIGMA2012 uses a highly customised version of ESRI's ArcGIS 10 and 10.1 with a fully relational Access 2007/2010 geodatabase. BGS•SIGMA2012 is the third external release of our award-winning digital field mapping toolkit. The first free external release of the award-winning digital field mapping toolkit was in 2009, with the third version (BGS-SIGMAmobile2012 v1.01) released on our website (http://www.bgs.ac.uk/research/sigma/home.html) in 2013. The BGS•SIGMAmobile toolkit formed the major part of the first two releases but this new version integrates the BGS•SIGMAdesktop functionality that BGS routinely uses to transform our field

  17. On Building and Processing of Large Digitalized Map Archive

    Directory of Open Access Journals (Sweden)

    Milan Simunek

    2011-07-01

    Full Text Available A tall list of problems needs to be solved during a long-time work on a virtual model of Prague aim of which is to show historical development of the city in virtual reality. This paper presents an integrated solution to digitalizing, cataloguing and processing of a large number of maps from different periods and from variety of sources. A specialized (GIS software application was developed to allow for a fast georeferencing (using an evolutionary algorithm, for cataloguing in an internal database, and subsequently for an easy lookup of relevant maps. So the maps could be processed further to serve as a main input for a proper modeling of a changing face of the city through times.

  18. Spectral features based tea garden extraction from digital orthophoto maps

    Science.gov (United States)

    Jamil, Akhtar; Bayram, Bulent; Kucuk, Turgay; Zafer Seker, Dursun

    2018-05-01

    The advancements in the photogrammetry and remote sensing technologies has made it possible to extract useful tangible information from data which plays a pivotal role in various application such as management and monitoring of forests and agricultural lands etc. This study aimed to evaluate the effectiveness of spectral signatures for extraction of tea gardens from 1 : 5000 scaled digital orthophoto maps obtained from Rize city in Turkey. First, the normalized difference vegetation index (NDVI) was derived from the input images to suppress the non-vegetation areas. NDVI values less than zero were discarded and the output images was normalized in the range 0-255. Individual pixels were then mapped into meaningful objects using global region growing technique. The resulting image was filtered and smoothed to reduce the impact of noise. Furthermore, geometrical constraints were applied to remove small objects (less than 500 pixels) followed by morphological opening operator to enhance the results. These objects served as building blocks for further image analysis. Finally, for the classification stage, a range of spectral values were empirically calculated for each band and applied on candidate objects to extract tea gardens. For accuracy assessment, we employed an area based similarity metric by overlapping obtained tea garden boundaries with the manually digitized tea garden boundaries created by experts of photogrammetry. The overall accuracy of the proposed method scored 89 % for tea gardens from 10 sample orthophoto maps. We concluded that exploiting the spectral signatures using object based analysis is an effective technique for extraction of dominant tree species from digital orthophoto maps.

  19. The genotype-phenotype map of an evolving digital organism.

    Directory of Open Access Journals (Sweden)

    Miguel A Fortuna

    2017-02-01

    Full Text Available To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences, which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

  20. The genotype-phenotype map of an evolving digital organism.

    Science.gov (United States)

    Fortuna, Miguel A; Zaman, Luis; Ofria, Charles; Wagner, Andreas

    2017-02-01

    To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10141 genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

  1. Digital Sound Encryption with Logistic Map and Number Theoretic Transform

    Science.gov (United States)

    Satria, Yudi; Gabe Rizky, P. H.; Suryadi, MT

    2018-03-01

    Digital sound security has limits on encrypting in Frequency Domain. Number Theoretic Transform based on field (GF 2521 – 1) improve and solve that problem. The algorithm for this sound encryption is based on combination of Chaos function and Number Theoretic Transform. The Chaos function that used in this paper is Logistic Map. The trials and the simulations are conducted by using 5 different digital sound files data tester in Wave File Extension Format and simulated at least 100 times each. The key stream resulted is random with verified by 15 NIST’s randomness test. The key space formed is very big which more than 10469. The processing speed of algorithm for encryption is slightly affected by Number Theoretic Transform.

  2. Coastline planning and management through digital mapping systems

    Science.gov (United States)

    Hysenaj, M.

    2015-11-01

    Albania is a country with a coastline of 316 km. The potentiality offered turns into a determinant factor for the Albanian economy. However specific issues need a solution. One of them remains the shoreline pollution. It affects mostly foreign visitors, also local population which recently tends to avoid attending these areas, instead they frequent foreign places. The importance of GIS technology in the water sector is undisputed. This paper will present a full set of digital maps representing a complete picture of the Albanian shoreline situation. The entire coastline is divided into the major frequented areas with a spatial extension based mainly on district level.

  3. Digital bedrock geologic map of parts of the Huntington, Richmond, Bolton and Waterbury quadrangles, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG95-9A Thompson, PJ�and Thompson, TB, 1995, Digital bedrock geologic map of parts of the Huntington, Richmond, Bolton and Waterbury quadrangles,...

  4. Digital and preliminary bedrock geologic map of the Wallingford quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG98-335A Burton, WC, and Ratcliffe, NM, 2000, Digital and preliminary bedrock geologic map of the Wallingford quadrangle, Vermont: USGS Open-File...

  5. Digital and preliminary bedrock geologic map of the Pico Peak quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG98-226A Walsh, G. J., and Ratcliffe, N.M., 1998,�Digital and preliminary bedrock geologic map of the Pico Peak quadrangle, Vermont: USGS...

  6. Digital and preliminary bedrock geologic map of the Chittenden quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG97-854A Ratcliffe, NM, 1997,�Digital and preliminary bedrock geologic map of the Chittenden quadrangle, Vermont: USGS Open-File Report 97-854, 1...

  7. Digital and preliminary bedrock geologic map of the Mount Carmel quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG98-330A Ratcliffe, N.M., and Walsh, G. J., 1998,�Digital and preliminary bedrock geologic map of the Mount Carmel quadrangle, Vermont: USGS...

  8. Digital bedrock geologic map of the Mount Snow & Readsboro quadrangles, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG95-DM1 Ratcliffe, NM, 1995, Digital bedrock geologic map of the Mount Snow & Readsboro quadrangles, Vermont, scale 1:24000, The bedrock...

  9. Digital bedrock geologic map of the Morrisville quadrangle,�Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG98-1 Springston, G., Kim, J., and Applegate, G.S., 1998,�Digital bedrock geologic map of the Morrisville quadrangle,�Vermont: VGS Open-File...

  10. Digital compilation bedrock geologic map of part of the Waitsfield quadrangle, Vermont

    Data.gov (United States)

    Vermont Center for Geographic Information — Digital Data from VG96-03�Digital compilation bedrock geologic map of part of the Waitsfield quadrangle, Vermont: VGS Open-File Report VG96-3A, 2 plates, scale...

  11. Digital Terroir Mapping in the Tokaj Historical Wine Region

    Science.gov (United States)

    Pásztor, László; Lukácsy, György; Szabó, József; László, Péter; Burai, Péter; Bakacsi, Zsófia; Koós, Sándor; Laborczi, Annamária; Takács, Katalin; Bekő, László

    2015-04-01

    Tokaj is a historical region for botritized dessert wine making, the famed Tokaji Wine Region has the distinction of being Europe's first classified wine region. Very recently the sustainable quality wine production in the region was targeted, which requires detailed and "terroir-based approach" characterization of viticultural land. Tokaj region consists of 27 villages, the total producing vineyard surface area is 5,500 hectares, and the total vineyard land exceeds 11,000 hectares. The Tokaj Kereskedőház Ltd. is the only state owned winery in Hungary. The company is integrating grapes for wine production from 1,100 hectares of vineyard, which consist of 3,500 parcels with average size of 0.3 hectares. In 2013 the Hungarian Government has decided to elaborate a sustainable quality wine production in the Tokaj region coordinated by the Tokaj Kereskedőház Ltd, the biggest wine producer. To achieve the target it is indispensable to assess the viticultural potential of the land. In 2013 the characterization of the vineyard land potential was started collecting detailed, up-to-date information on the main environmental factors (geology, geomorphology and soil) which comprise the terroir effect and combined with legacy data of climate. The Council of Wine Communities of Tokaj Region has decided to widen the survey for the whole wine region in the year 2014. The primary objective of our work was the execution of an appropriate terroir zoning, which was carried out by digital terroir mapping. As a start-up we adapted some concepts recently applied in French wine regions. The implementation was however carried out totally in spatial, digital environment. Four main sources of information have been used (i) airborne laser scanning, (ii) hyperspectral imaginary, (iii) digital soil maps compiled based on detailed soil survey and (iv) interpolated climatic data. Based on them pedoclimate, mesoclimate and soil water reservoir were spatially predicted. The operational spatial

  12. Comparing the performance of various digital soil mapping approaches to map physical soil properties

    Science.gov (United States)

    Laborczi, Annamária; Takács, Katalin; Pásztor, László

    2015-04-01

    Spatial information on physical soil properties is intensely expected, in order to support environmental related and land use management decisions. One of the most widely used properties to characterize soils physically is particle size distribution (PSD), which determines soil water management and cultivability. According to their size, different particles can be categorized as clay, silt, or sand. The size intervals are defined by national or international textural classification systems. The relative percentage of sand, silt, and clay in the soil constitutes textural classes, which are also specified miscellaneously in various national and/or specialty systems. The most commonly used is the classification system of the United States Department of Agriculture (USDA). Soil texture information is essential input data in meteorological, hydrological and agricultural prediction modelling. Although Hungary has a great deal of legacy soil maps and other relevant soil information, it often occurs, that maps do not exist on a certain characteristic with the required thematic and/or spatial representation. The recent developments in digital soil mapping (DSM), however, provide wide opportunities for the elaboration of object specific soil maps (OSSM) with predefined parameters (resolution, accuracy, reliability etc.). Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil map. This suggests the opportunity of optimization. For the creation of an OSSM one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). We started comprehensive analysis of the effects of the various DSM components on the accuracy of the output maps on pilot areas. The aim of this study is to compare and evaluate different

  13. Digital field mapping of the Dingle Peninsular, County Kerry, Ireland

    Science.gov (United States)

    Tanner, David; Bense, Frithjof

    2014-05-01

    In September 2011, a team of eight students from the University of Göttingen digitally mapped seven 10 km2 adjoining areas on the western tip of the Dingle Peninsular in County Kerry, Ireland for their M.Sc. mapping projects. The students worked in pairs; each pair was equipped with an outdoor, waterproof, drop-proof touchscreen tablet running Windows and Midland Valley Exploration Ltd's Fieldmove software. They also used paper field-notebooks, cameras and hand compasses. The tablets have built-in GPS, two five-hour batteries, and displays that are designed to work even in bright sunlight. In preparation for the fieldwork, the topographic maps of the area (from 1890!) were scanned, geo-rectified and draped onto the DEM of the area using the Midland Valley's Move software. The geology of the Dingle Peninsular is complex; an inlier of Ordovician rocks that were deformed in the Caledonian Orogeny, are surrounded by Devonian Old Red Sandstone (ORS) units, which were syntectonically deposited as the whole area was folded during the Variscan Orogeny. Consequently the ORS units vary in thickness tremendously and facies often vary laterally. The ORS also contains many unconformities. The area is excellently exposed at the coastline, but it is poor inland because of glacial deposits. As a consequent the students required the software to record bedding planes, cleavages, fold axes and unconformities, as well as standard geological information. The work went well, despite the weather (the post tropical cyclone Katia!). It was far quicker to complete the map compared to working on a paper map, after the students had got used to the software and the tablet controls. The GPS in the tablet was deemed to be inaccurate and locations on the map were ascertained using standard techniques. It was also extremely useful to export tectonic data in the evening for stereonet projection analysis. Each 10 km2 area was mapped at 1:10000 in approx. 2 weeks. Because the tablet requires two

  14. Digital Technology in the protection of cultural heritage Bao Fan Temple mural digital mapping survey

    Directory of Open Access Journals (Sweden)

    Y. Zheng

    2015-08-01

    Full Text Available Peng Xi county, Sichuan province, the Bao Fan temple mural digitization survey mapping project: we use three-dimensional laserscanning, multi-baseline definition digital photography, multi-spectral digital image acquisition and other technologies for digital survey mapping. The purpose of this project is to use modern mathematical reconnaissance mapping means to obtain accurate mural shape, color, quality and other data. Combined with field investigation and laboratory analysis results, and based on a comprehensive survey and study, a comprehensive analysis of the historical Bao Fan Temple mural artistic and scientific value was conducted. A study of the mural’s many qualities (structural, material, technique, preservation environment, degradation, etc. reveal all aspects of the information carried by the Bao Fan Temple mural. From multiple angles (archeology, architecture, surveying, conservation science and other disciplines an assessment for the Bao Fan Temple mural provides basic data and recommendations for conservation of the mural. In order to achieve the conservation of cultural relics in the Bao Fan Temple mural digitization survey mapping process, we try to apply the advantages of three-dimensional laser scanning equipment. For wall murals this means obtaining three-dimensional scale data from the scan of the building and through the analysis of these data to help determine the overall condition of the settlement as well as the deformation of the wall structure. Survey analysis provides an effective set of conclusions and suggestions for appropriate mural conservation. But before data collection, analysis and research need to first to select the appropriate scanning equipment, set the appropriate scanning accuracy and layout position of stations necessary to determine the scope of required data. We use the fine features of the three-dimensional laser scanning measuring arm to scan the mural surface deformation degradation to reflect

  15. Digital Technology in the protection of cultural heritage Bao Fan Temple mural digital mapping survey

    Science.gov (United States)

    Zheng, Y.

    2015-08-01

    Peng Xi county, Sichuan province, the Bao Fan temple mural digitization survey mapping project: we use three-dimensional laserscanning, multi-baseline definition digital photography, multi-spectral digital image acquisition and other technologies for digital survey mapping. The purpose of this project is to use modern mathematical reconnaissance mapping means to obtain accurate mural shape, color, quality and other data. Combined with field investigation and laboratory analysis results, and based on a comprehensive survey and study, a comprehensive analysis of the historical Bao Fan Temple mural artistic and scientific value was conducted. A study of the mural's many qualities (structural, material, technique, preservation environment, degradation, etc.) reveal all aspects of the information carried by the Bao Fan Temple mural. From multiple angles (archeology, architecture, surveying, conservation science and other disciplines) an assessment for the Bao Fan Temple mural provides basic data and recommendations for conservation of the mural. In order to achieve the conservation of cultural relics in the Bao Fan Temple mural digitization survey mapping process, we try to apply the advantages of three-dimensional laser scanning equipment. For wall murals this means obtaining three-dimensional scale data from the scan of the building and through the analysis of these data to help determine the overall condition of the settlement as well as the deformation of the wall structure. Survey analysis provides an effective set of conclusions and suggestions for appropriate mural conservation. But before data collection, analysis and research need to first to select the appropriate scanning equipment, set the appropriate scanning accuracy and layout position of stations necessary to determine the scope of required data. We use the fine features of the three-dimensional laser scanning measuring arm to scan the mural surface deformation degradation to reflect the actual state of

  16. Digital Geologic Mapping and Integration with the Geoweb: The Death Knell for Exclusively Paper Geologic Maps

    Science.gov (United States)

    House, P. K.

    2008-12-01

    The combination of traditional methods of geologic mapping with rapidly developing web-based geospatial applications ('the geoweb') and the various collaborative opportunities of web 2.0 have the potential to change the nature, value, and relevance of geologic maps and related field studies. Parallel advances in basic GPS technology, digital photography, and related integrative applications provide practicing geologic mappers with greatly enhanced methods for collecting, visualizing, interpreting, and disseminating geologic information. Even a cursory application of available tools can make field and office work more enriching and efficient; whereas more advanced and systematic applications provide new avenues for collaboration, outreach, and public education. Moreover, they ensure a much broader audience among an immense number of internet savvy end-users with very specific expectations for geospatial data availability. Perplexingly, the geologic community as a whole is not fully exploring this opportunity despite the inevitable revolution in portends. The slow acceptance follows a broad generational trend wherein seasoned professionals are lagging behind geology students and recent graduates in their grasp of and interest in the capabilities of the geoweb and web 2.0 types of applications. Possible explanations for this include: fear of the unknown, fear of learning curve, lack of interest, lack of academic/professional incentive, and (hopefully not) reluctance toward open collaboration. Although some aspects of the expanding geoweb are cloaked in arcane computer code, others are extremely simple to understand and use. A particularly obvious and simple application to enhance any field study is photo geotagging, the digital documentation of the locations of key outcrops, illustrative vistas, and particularly complicated geologic field relations. Viewing geotagged photos in their appropriate context on a virtual globe with high-resolution imagery can be an

  17. Stress field modelling from digital geological map data

    Science.gov (United States)

    Albert, Gáspár; Barancsuk, Ádám; Szentpéteri, Krisztián

    2016-04-01

    To create a model for the lithospheric stress a functional geodatabase is required which contains spatial and geodynamic parameters. A digital structural-geological map is a geodatabase, which usually contains enough attributes to create a stress field model. Such a model is not accurate enough for engineering-geological purposes because simplifications are always present in a map, but in many cases maps are the only sources for a tectonic analysis. The here presented method is designed for field geologist, who are interested to see the possible realization of the stress field over the area, on which they are working. This study presents an application which can produce a map of 3D stress vectors from a kml-file. The core application logic is implemented on top of a spatially aware relational database management system. This allows rapid and geographically accurate analysis of the imported geological features, taking advantage of standardized spatial algorithms and indexing. After pre-processing the map features in a GIS, according to the Type-Property-Orientation naming system, which was described in a previous study (Albert et al. 2014), the first stage of the algorithm generates an irregularly spaced point cloud by emitting a pattern of points within a user-defined buffer zone around each feature. For each point generated, a component-wise approximation of the tensor field at the point's position is computed, derived from the original feature's geodynamic properties. In a second stage a weighted moving average method calculates the stress vectors in a regular grid. Results can be exported as geospatial data for further analysis or cartographic visualization. Computation of the tensor field's components is based on the implementation of the Mohr diagram of a compressional model, which uses a Coulomb fracture criterion. Using a general assumption that the main principal stress must be greater than the stress from the overburden, the differential stress is

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DODGE COUNTY, WISCONSIN (AND INCORPORATED AREAS) - Fox Lake Physical Map Revision

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  19. ANALYSIS OF THE EFFECTS OF IMAGE QUALITY ON DIGITAL MAP GENERATION FROM SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    H. Kim

    2012-07-01

    Full Text Available High resolution satellite images are widely used to produce and update a digital map since they became widely available. It is well known that the accuracy of digital map produced from satellite images is decided largely by the accuracy of geometric modelling. However digital maps are made by a series of photogrammetric workflow. Therefore the accuracy of digital maps are also affected by the quality of satellite images, such as image interpretability. For satellite images, parameters such as Modulation Transfer Function(MTF, Signal to Noise Ratio(SNR and Ground Sampling Distance(GSD are used to present images quality. Our previous research stressed that such quality parameters may not represent the quality of image products such as digital maps and that parameters for image interpretability such as Ground Resolved Distance(GRD and National Imagery Interpretability Rating Scale(NIIRS need to be considered. In this study, we analyzed the effects of the image quality on accuracy of digital maps produced by satellite images. QuickBird, IKONOS and KOMPSAT-2 imagery were used to analyze as they have similar GSDs. We measured various image quality parameters mentioned above from these images. Then we produced digital maps from the images using a digital photogrammetric workstation. We analyzed the accuracy of the digital maps in terms of their location accuracy and their level of details. Then we compared the correlation between various image quality parameters and the accuracy of digital maps. The results of this study showed that GRD and NIIRS were more critical for map production then GSD, MTF or SNR.

  20. DIGITAL

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. Comparison between detailed digital and conventional soil maps of an area with complex geology

    Directory of Open Access Journals (Sweden)

    Osmar Bazaglia Filho

    2013-10-01

    Full Text Available Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000 of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs. For the Digital Soil Map (DSM, spectral data were extracted from Landsat 5 Thematic Mapper (TM imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS. To compare the conventional and digital (DSM soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

  2. Constructing a Soil Class Map of Denmark based on the FAO Legend Using Digital Techniques

    DEFF Research Database (Denmark)

    Adhikari, Kabindra; Minasny, Budiman; Greve, Mette Balslev

    2014-01-01

    Soil mapping in Denmark has a long history and a series of soil maps based on conventional mapping approaches have been produced. In this study, a national soil map of Denmark was constructed based on the FAO–Unesco Revised Legend 1990 using digital soil mapping techniques, existing soil profile......) confirmed that the output is reliable and can be used in various soil and environmental studies without major difficulties. This study also verified the importance of GlobalSoilMap products and a priori pedological information that improved prediction performance and quality of the new FAO soil map...

  3. Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps

    KAUST Repository

    Parkes, Stephen

    2016-10-25

    Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.

  4. Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps

    KAUST Repository

    Parkes, Stephen; McCabe, Matthew; Al-Mashhawari, Samir K.; Rosas, Jorge

    2016-01-01

    Crop height measured from UAVs fitted with commercially available RGB cameras provide an affordable alternative to retrieve field scale high resolution estimates. The study presents an assessment of between flight reproducibility of Crop Surface Maps (CSM) extracted from Digital Surface Maps (DSM) generated by Structure from Motion (SfM) algorithms. Flights were conducted over a centre pivot irrigation system covered with an alfalfa crop. An important step in calculating the absolute crop height from the UAV derived DSM is determining the height of the underlying terrain. Here we use automatic thresholding techniques applied to RGB vegetation index maps to classify vegetated and soil pixels. From interpolation of classified soil pixels, a terrain map is calculated and subtracted from the DSM. The influence of three different thresholding techniques on CSMs are investigated. Median Alfalfa crop heights determined with the different thresholding methods varied from 18cm for K means thresholding to 13cm for Otsu thresholding methods. Otsu thresholding also gave the smallest range of crop heights and K means thresholding the largest. Reproducibility of median crop heights between flight surveys was 4-6cm for all thresholding techniques. For the flight conducted later in the afternoon shadowing caused soil pixels to be classified as vegetation in key locations around the domain, leading to lower crop height estimates. The range of crop heights was similar for both flights using K means thresholding (35-36cm), local minimum thresholding depended on whether raw or normalised RGB intensities were used to calculate vegetation indices (30-35cm), while Otsu thresholding had a smaller range of heights and varied most between flights (26-30cm). This study showed that crop heights from multiple survey flights are comparable, however, they were dependent on the thresholding method applied to classify soil pixels and the time of day the flight was conducted.

  5. Digital Geologic Map Database of Medicine Lake Volcano, Northern California

    Science.gov (United States)

    Ramsey, D. W.; Donnelly-Nolan, J. M.; Felger, T. J.

    2010-12-01

    Medicine Lake volcano, located in the southern Cascades ~55 km east-northeast of Mount Shasta, is a large rear-arc, shield-shaped volcano with an eruptive history spanning nearly 500 k.y. Geologic mapping of Medicine Lake volcano has been digitally compiled as a spatial database in ArcGIS. Within the database, coverage feature classes have been created representing geologic lines (contacts, faults, lava tubes, etc.), geologic unit polygons, and volcanic vent location points. The database can be queried to determine the spatial distributions of different rock types, geologic units, and other geologic and geomorphic features. These data, in turn, can be used to better understand the evolution, growth, and potential hazards of this large, rear-arc Cascades volcano. Queries of the database reveal that the total area covered by lavas of Medicine Lake volcano, which range in composition from basalt through rhyolite, is about 2,200 km2, encompassing all or parts of 27 U.S. Geological Survey 1:24,000-scale topographic quadrangles. The maximum extent of these lavas is about 80 km north-south by 45 km east-west. Occupying the center of Medicine Lake volcano is a 7 km by 12 km summit caldera in which nestles its namesake, Medicine Lake. The flanks of the volcano, which are dotted with cinder cones, slope gently upward to the caldera rim, which reaches an elevation of nearly 2,440 m. Approximately 250 geologic units have been mapped, only half a dozen of which are thin surficial units such as alluvium. These volcanic units mostly represent eruptive events, each commonly including a vent (dome, cinder cone, spatter cone, etc.) and its associated lava flow. Some cinder cones have not been matched to lava flows, as the corresponding flows are probably buried, and some flows cannot be correlated with vents. The largest individual units on the map are all basaltic in composition, including the late Pleistocene basalt of Yellowjacket Butte (296 km2 exposed), the largest unit on the

  6. Preliminary digital geologic maps of the Mariposa, Kingman, Trona, and Death Valley Sheets, California

    International Nuclear Information System (INIS)

    D'Agnese, F.A.; Faunt, C.C.; Turner, A.K.

    1995-01-01

    Parts of four 1:250,000-scale geologic maps by the California Department of Natural Resources, Division of Mines and Geology have been digitized for use in hydrogeologic characterization. These maps include the area of California between lat. 35 degree N; Long. 115 degree W and lat. 38 degree N, long. 118 degree W of the Kingman Sheet (Jennings, 1961), Trona Sheet (Jennings and others, 1962), Mariposa Sheet (Strand, 1967), and Death Valley Sheet (Streitz and Stinson, 1974). These digital maps are being released by the US Geological Survey in the ARC/INFO Version 6.1 Export format. The digitized data include geologic unit boundaries, fault traces, and identity of geologic units. The procedure outlined in US Geological Survey Circular 1054 (Soller and others, 1990) was sued during the map construction. The procedure involves transferring hard-copy data into digital format by scanning manuscript maps, manipulating the digital map data, and outputting the data. Most of the work was done using Environmental Systems Research Institute's ARC/INFO software. The digital maps are available in ARC/INFO Rev. 6.1 Export format, from the USGS, Yucca Mountain Project, in Denver, Colorado

  7. Insights into bird wing evolution and digit specification from polarizing region fate maps.

    Science.gov (United States)

    Towers, Matthew; Signolet, Jason; Sherman, Adrian; Sang, Helen; Tickle, Cheryll

    2011-08-09

    The proposal that birds descended from theropod dinosaurs with digits 2, 3 and 4 was recently given support by short-term fate maps, suggesting that the chick wing polarizing region-a group that Sonic hedgehog-expressing cells-gives rise to digit 4. Here we show using long-term fate maps that Green fluorescent protein-expressing chick wing polarizing region grafts contribute only to soft tissues along the posterior margin of digit 4, supporting fossil data that birds descended from theropods that had digits 1, 2 and 3. In contrast, digit IV of the chick leg with four digits (I-IV) arises from the polarizing region. To determine how digit identity is specified over time, we inhibited Sonic hedgehog signalling. Fate maps show that polarizing region and adjacent cells are specified in parallel through a series of anterior to posterior digit fates-a process of digit specification that we suggest is involved in patterning all vertebrate limbs with more than three digits.

  8. Digital geologic map database of the Nevada Test Site area, Nevada

    Science.gov (United States)

    Wahl, R.R.; Sawyer, D.A.; Minor, S.A.; Carr, M.D.; Cole, J.C.; Swadley, W.C.; Laczniak, R.J.; Warren, R.G.; Green, K.S.; Engle, C.M.

    1997-01-01

    Forty years of geologic investigations at the Nevada Test Site (NTS) have been digitized. These data include all geologic information that: (1) has been collected, and (2) can be represented on a map within the map borders at the map scale is included in the map digital coverages. The following coverages are included with this dataset: Coverage Type Description geolpoly Polygon Geologic outcrops geolflts line Fault traces geolatts Point Bedding attitudes, etc. geolcald line Caldera boundaries geollins line Interpreted lineaments geolmeta line Metamorphic gradients The above coverages are attributed with numeric values and interpreted information. The entity files documented below show the data associated with each coverage.

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Northampton County, Virginia (And Incorporated Areas)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHARLES CITY COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, TEXAS (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ALBANY COUNTY, WYOMING (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EAU CLAIRE COUNTY PMR, WISCONSIN, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk;...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MOHAVE COUNTY, ARIZONA (AND INCORPORATED AREAS)

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NAVARRO COUNTY, TEXAS (AND INCORPORATED AREAS)

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, McCURTAIN COUNTY, OKLAHOMA AND INCORPORATED AREAS

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COMMONWEALTH OF PUERTO RICO, PUERTO RICO

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, KENDALL COUNTY AND INCORPORATED AREAS, TEXAS, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DALE COUNTY, ALABAMA AND INCORPORATED AREAS

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEVIER COUNTY, TENNESSEE (AND INCORPORATED AREAS)

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, GRAINGER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, TAYLOR COUNTY, TX AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHURCHILL COUNTY, NEVADA (AND INCORPORATED AREAS)

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, COMAL COUNTY AND INCORPORATED AREAS, TEXAS, USA

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    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, FORSYTH COUNTY, GEORGIA, AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ARLINGTON COUNTY, VA, USA, AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SANTA FE COUNTY, NEW MEXICO

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CAPE GIRARDEAU COUNTY, MISSOURI (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Otero COUNTY and Incorporated Areas, NEW MEXICO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, Lee County, Alabama and Incorporated Areas

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CLARKE COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FlOOD INSURANCE RATE MAP DATABASE, CHESTER COUNTY, SC, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts Flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JONES COUNTY, MISSISSIPPI AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, VALENCIA COUNTY, NEW MEXICO, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CHESTER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MCNAIRY COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SUMNER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MEIGS COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEWIS COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JACKSON COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  1. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, SEQUATCHIE COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  2. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PICKETT COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  3. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, WEAKLEY COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  4. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, MARION COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  5. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, DYER COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  6. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, UNICOI COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  7. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, HANCOCK COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  8. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RHEA COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  9. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PERRY COUNTY, TENNESSEE AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  10. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, NANTUCKET COUNTY, MASSACHUSETTS (ALL JURISDICTIONS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  11. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, ORANGE COUNTY, FLORIDA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  12. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, BREVARD COUNTY, FLORIDA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  13. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, CANYON COUNTY, IDAHO (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  14. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, San Bernardino COUNTY, CALIFORNIA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  15. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, PICKENS COUNTY, SOUTH CAROLINA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  16. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, EAST BATON ROUGE PARISH, LOUISIANA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  17. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, RICHLAND COUNTY, SOUTH CAROLINA AND INCORPORATED AREAS

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  18. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, LEWIS COUNTY, WEST VIRGINIA (AND INCORPORATED AREAS)

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  19. DIGITAL FLOOD INSURANCE RATE MAP DATABASE,FREDERICK COUNTY, VA, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk...

  20. DIGITAL FLOOD INSURANCE RATE MAP DATABASE, JEFFERSON COUNTY, NEW YORK, USA

    Data.gov (United States)

    Federal Emergency Management Agency, Department of Homeland Security — The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk Information And supporting data used to develop the risk data. The primary risk;...