The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.
GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.
The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.
The Digital Geologic-GIS Map of Grant's Headquarters at City Point, Petersburg National Battlefield, Virginia is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (cipo_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (cipo_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (cipo_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (pete_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (pete_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (cipo_geology_metadata_faq.pdf). Please read the pete_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cipo_geology_metadata.txt or cipo_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).
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Locations of corporate headquarters in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.
These data show sample locations for various abiotic data collected on Konza Prairie (rain gauges, soil moisture, and stream data). Included in these data are the locations for 12 rain gauges (GIS300) on Konza Prairie. The Konza headquarters weather station formerly consisted of two gauges which were operated year-round. The Konza headquarters weather station currently consists of one Otto-Pluvio2 gauge which is operated year-round. The remaining Konza-operated gauges run from April 1 to November 1. These data are to be used in conjunction with the APT01 (precipitation) dataset. GIS305 defines the locations where measurements of soil moisture (%volume) are taken on Konza Prairie. These data are to be used in conjunction with the ASM01 (soil moisture) dataset. GIS309 defines the locations within watershed N4D of soil sampler nests. In Jan 2020, we separated the original GIS310 file 'Wells in N4D' into GIS310 'Wells in N4D' and GIS309 'Soil Sampler Nests'. Prior to then, soil sampler nests and wells were combined in GIS310. GIS310 defines the locations within watershed N4D where samples are taken for analyzing the belowground water chemistry of the watershed. These data are to be used in conjunction with the AGW01 dataset. GIS311 defines the locations of 14 wells at two sites along Kings Creek. Depth and nutrient content of groundwater is measured at these sites. These data are to be used in conjunction with the AGW02 dataset. GIS315 defines the locations of stream sampling stations within multiple Konza watersheds. These data are to be used in conjunction with the NWC, ASS, ASD, and ASW datasets. GIS320 defines the locations of the rainfall collectors used to collect the samples analyzed as a part of the National Atmospheric Deposition Program. These data are to be used in conjunction with the ANA01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).
This dataset contains point locations for all publicly identified sites and office locations including headquarters, station, field office and investigative unit locations. This dataset was created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO), MPD and participating D.C. government agencies. Facilities and offices were obtained from MPD's Office of Corporate Communications, through interviews with MPD's Criminal Intelligence, and Tactical Crime Analysis Unit and through site surveys conducted by DC GIS staff.
This dataset contains a GIS database of Aids to Navigation in the Gulf of America and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas. These data were compiled on 1999-10-21. The term "Aids to Navigation" (ATONS or AIDS) refers to a device outside of a vessel used to assist mariners in determining their position or safe course, or to warn them of obstructions. AIDS to navigation include lighthouses, lights, buoy, sound signals, landmarks, RACONs, radio beacons, LORAN, and omega. These include AIDS which are installed and maintained by the Coast Guard as well as privately installed and maintained aids (permit required). This does not include unofficial AIDS (illegal) such as stakes, PVC pipes, and such placed without permission. Each USCG District Headquarters is responsible for updating their database on an "as needed" basis. When existing AIDS are destroyed or relocated and new AIDS are installed the database is updated. Each AID is assigned an official "light listing number". The light list is a document listing the current status of ATONS and it is published and distributed on a regular basis. Interim changes to the light list are published in local Notices to Mariners which are the official means which navigators are supposed to keep their charts current. In addition, the USCG broadcasts Notices to Mariners on the marine band radio as soon as changes of the status of individual AIDS are reported. The light list number and local Notices to Mariners reports are suggested ways to keep the database current on a regular or even "real time" basis. However, annual (or more frequent) updates of the entire dataset may be obtained from each USCG District Headquarters. Geographic Information System (GIS) software is required to display the data in this NCEI accession.
This dataset is intended to provide a statewide depiction of CALFIRE and contract facilities for fire suppression. It includes state and local funded fire stations, stations providing wildland firefighting services to the state under contract Schedule A agreements, air attack bases and helitack bases, conservation camps, youth conservation camps, lookouts, nurseries, operations centers, regional headquarters, unit headquarters, state forest visitor centers, fire centers, and communications infrastructure.This dataset is specific to state and local fire stations and other facilities used for wildland fire protection within California. It does not include all fire stations that could potentially be used for such purposes (e.g., federal).This service represents the latest release of the dataset, and is updated annually when a new version is released. As of May 2024 it represents facility24_1.Note: This dataset includes decommissioned facilities. Use the FACILITY_STATUS field to query appropriately.
This coverage was obtained in digital form from Chris Barton of the USGS. Bedrock geology in the Hubbard Brook Valley was mapped by C.C. Barton, R.H. Comerlo, and S.W. Bailey, August 1994 to August 1995. The Map is entitled "BEDROCK GEOLOGIC MAP OF HUBBARD BROOK EXPERIMENTAL FOREST AND MAPS OF FRACTURES AND GEOLOGY IN ROADCUTS ALONG INTERSTATE 93, GRAFTON COUNTY, NEW HAMPSHIRE" and was approved for publication on August 28, 1995. The benchmark adjacent to the Hubbard Brook Headquarters building was set by the New Hampshire Department of Transportation in 1993. The station is a standard NHDOT disk stamped "Hubbard Brook 1993", set into the top of a 4 FT long by 5 IN granite monument flush with the ground and level with the parking lot. Located 17.5 FT (5.3 M) southeast from the southeast corner post for a fence and the orange carsonite marker, 49.5 FT (15.1 M) northeast from the north corner of the office building, 60 FT (18.3 M) east from the west face of the concrete curb, 51.0 FT (15.5 M) south from the northeast corner post for a fence. Benchmark location: NAD83 Latitude = 43 56 38.0361 Longitude = 71 42 03.9897 Northing = 160408.164 Meters Easting = 297235.062 Meters NAD27 Latitude = 43 56 37.7891 Longitude = 71 42 05.7012 Northing = 526,241.45 Feet (State Plane Zone 4676) Easting = 490,803.21 Feet Elevation = 833.87 Feet NGVD29 (El order 31). Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N
Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant _location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.
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State Government Buildings in the United States This dataset is comprised of buildings or properties that are owned or leased by state level governments. It includes buildings occupied by the headquarters of cabinet level state government executive departments, legislative office buildings outside of the capitol building, offices and court rooms associated with the highest level of the judicial branch of the state government, and large multi-agency state office buildings. Because the research to create this dataset was primarily keyed off of the headquarters of cabinet level state government agencies, some state office buildings that don't house a headquarters for such an agency may have been excluded. Intentionally excluded from this dataset are government run institutions (e.g., schools, colleges, prisons, and libraries). Also excluded are state capitol buildings, as these entities are represented in other HSIP layers. State owned or leased buildings whose primary purpose is not to house state offices have also been intentionally excluded from this dataset. Examples of these include "Salt Domes", "Park Shelters", and "Highway Garages". All entities that have been verified to have no building name have had their [NAME] value set to "NO NAME". If the record in the original source data had no building name and TGS was unable to verify the building name, the [NAME] value was set to "UNKNOWN". All phone numbers in this dataset have been verified by TGS to be the main phone for the building. If the building was verified not to have a main phone number, the [TELEPHONE] field has been left blank. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 11/27/2007 and the newest record dates from 05/28/2008.
Cartographer and GIS expert. Proven track of commercial experience. Since 2001, the leader of teams specializing in designing and maintaining spatial databases for navigation systems and modeling topographical data. Knowledge of Polish Spatial Data Infrastructure. Polish National Topographical Database model designer. Directly involved in the design and implementation of the Spatial Data Infrastructure in Poland. Vice-dean for Science and Development at the Faculty of Geodesy and Cartography at Warsaw University of Technology (2012-2016). Vice-Dean for Development and Cooperation with the Economy at the Faculty of Geodesy and Cartography at Warsaw University of Technology (2020-2024). Originator and project manager of the creation of the Center for Geospatial Analysis and Satellite Computing (CENAGIS). Advisor (expert) to the Head Office of Geodesy and Cartography in Poland (from 1999) in the SDI area. The initiator of the establishment of the Laboratory of Mobile Cartography and author of the teaching program in the field of Geoinformatics at Warsaw University of Technology. More than ten years of experience in managing the work of GIS department and GIS Database Operation Department (Director) in the capital group of PPWK/Mobile Internet Technology (joint-stock company) (among many tasks, several years of cooperation with Google Company - delivering of spatial dataset for the Polish territory). Membership of professional bodies (selected): • The Polish National Committe for International Cartographic Association (from 2004) • The Association of Polish Cartographers (from 1999, from 2013 Member of the Board) • The Geoinformatics Commision of the Polish Academy of Arts and Sciences (from 2016) • The Committee on Geodesy of the Polish Academy of Sciences, The Chair of Geoinformation Section (from 2016) • The Scientific Council of Polish Polar Consortium (2014-2022) • The Chairman of The Working Group "Smart networks and geoinformation technologies" (The Polish Smart Specialization) at the Ministry of Development (2015-2022) • V-Ce Chairman Of National Council For Spatial Information In Poland (From 2018) (inter-ministeral committee)
Grand Teton National Park, Headquarters - Public for Open DataNational Park Service Open DataIRMA Data Store Reference
This layer contains the boundaries of the VDOT Area Headquarters (AHQ) zones of responsibility. These areas are important as they designate which AHQ is responsible for road maintenance in that area. Additionally, in certain areas of Virginia (the Virginia independent cities, and Arlington and Henrico Counties), maintenance responsibility of local roads is devolved to the local government. These areas have been been removed.July 2016 changes - changed Residency and Residency Codes for the AHQ's in the new Residencies of Louisa and AbingdonChanges made in July 2015 to change several Area HQ boundaries for Leesburg Residency / Loudoun County. These changes result in some non-contiguous island and donut polygons. These are necessary to transfer certain unpaved routes from one Area to another. Changes made in Franklin Co.
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MIT Licensehttps://opensource.org/licenses/MIT
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This Coastal Barrier Resources System (CBRS) data set, produced by the U.S. Fish and Wildlife Service (Service), contains areas designated as undeveloped coastal barriers in accordance with the Coastal Barrier Resources Act (CBRA), as amended (16 U.S.C. 3501 et seq.). These digital polygons are representations of the CBRS boundaries shown on the official CBRS maps referenced in 16 U.S.C. 3503(a). Copies of the official CBRS maps are available for viewing at Service’s Headquarters office and are also available to view or download at https://www.fws.gov/cbra/maps/index.html. The boundaries used to create the polygons herein were compiled between 12/6/2013 and 8/16/2023 from the official CBRS maps. The boundaries of the CBRS Units in Connecticut, Massachusetts, Rhode Island, and the Long Island portion of New York, were digitized from the official paper maps according to the guidelines in a notice published in the Federal Register on August 29, 2013 (see the “Georeferencing and Boundary Interpretation” and “Boundary Transcription” sections of 78 FR 53467; available at https://www.federalregister.gov/d/2013-21167). In all other cases where the official map was created through digital methods, the digital boundary was used. CBRS boundaries viewed using the CBRS Mapper or shapefiles are subject to misrepresentations beyond the Service’s control, including misalignments of the boundaries with third party base layers and misprojections of spatial data. The Service is not responsible for any misuse or misinterpretation of this digital data set, including use of the data to determine eligibility for Federal funding or financial assistance. Users should pair these data with the CBRS Buffer Zone shapefile and an orthoimage when inspecting areas that are within or in close proximity to the CBRS. Properties or structures that fall partially or entirely within the buffer area may be within the CBRS, and an official determination from the Service is recommended. For an official determination of whether or not an area or specific property is located within the CBRS, please follow the procedures found at https://www.fws.gov/service/coastal-barrier-resources-system-property-documentation. The official CBRS map is the controlling document and should be consulted for all official determinations in close proximity (within 20 feet) of a CBRS boundary. For any questions regarding the CBRS, please contact your local Service field office or email CBRA@fws.gov. Contact information for Service field offices can be found at https://www.fws.gov/node/267216.
The Police Stations layer shows the point locations of law enforcement and sheriff offices in Massachusetts, covering local, county and state jurisdictions. The Massachusetts Emergency Management Agency (MEMA) GIS Program in cooperation with the Regional Planning Agencies and participating communities created the original data as part of the development of Homeland Security Data Layers. MassGIS has since incorporated updates into the data.The features represented include municipal police stations and Massachusetts State Police barracks. Although sheriffs are not technically charged with the same law enforcement tasks as local and state police, county sheriff headquarters are also included in this layer. The duties of the sheriffs include the management and operation of regional correctional systems and transportation of prisoners, service of judicial process and delivery of legal documents needed to support the operation of the courts, community policing, running various outreach services, and the enforcement of laws enacted for the public safety, health and welfare of the people. Not included in this layer are Environmental Police, campus police and various state and federal level law enforcement locations.Feature service also available.More details...
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Analysis of ‘Alameda County Land Use Survey 2006’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d42b51b1-d005-4f84-b011-865378ecf23a on 26 January 2022.
--- Dataset description provided by original source is as follows ---
--- Original source retains full ownership of the source dataset ---
This data set delineates the boundaries of the U.S. Fish and Wildlife Service geographic Regions. The dataset was created as a geographic representation of the Regional administrative boundaries of the US Fish and Wildlife Service at a very coarse scale. The boundaries were created using the ArcGIS shoreline dataset from approximately 1995. This dataset should not be used for legal purposes or at small scales and does not accurately denote the shorelines of the united states. The Regional Boundaries data set is managed by the FWS Headquarters Information Resources and Technology Management, Branch of Geospatial Data Management. The complete data and metadata can be accessed here: https://catalog.data.gov/dataset/us-fish-and-wildlife-service-regional-boundaries. This data set is a graphical representation and has limitations of accuracy as determined by, among others, the source, scale and resolution of the data. DOI Interior Regions / Regional Boundaries (https://fws.maps.arcgis.com/home/item.html?id=309aa728d6c041ceaefc1526a409b5d1).
This web map shows the location and details of the ICAC Headquarters and Regional Offices in Hong Kong. It is a subset of data made available by the Independent Commission Against Corruption under the Government of Hong Kong Special Administrative Region (the “Government”) at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is processed and converted to Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort. For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.
This map is designated as Final.
Land-Use Data Quality Control
Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.
Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.
Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.
The 2012 Sonoma County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Sonoma County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during July - September 2012 by staff visiting each field and noting what was grown. The county was divided into five survey areas using major road as centerlines and other geographic features for boundaries. The county was surveyed with two teams. The linework was heads up digitized in ArcGIS 10.0 with 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Field Boundaries were reviewed with ArcGIS 10.2 and NAIP 2012 imagery when it became available. The data was recombined after it was finished. The Virtual Basic Landuse Attributor was used for the survey and to start the post survey process; after converting to ArcGIS 10.2, the domain file geodatabase structure was used to attribute and help finish facilitating the post survey process. Tables were run through a Python script to put the data in the standard landuse format. ArcGIS geoprocessing tools and topology rules were used to locate errors and for quality control and assurance. Horse pastures were designated either S2 or S6. The special condition 'G' was used to denote vineyards that had sprinklers for frost protection rather than representing a cover crop as stated in the February 2009 Standard Land Use Legend used for this survey. Field Boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. GPS units connected to the laptops were used to confirm surveyor's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land use. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.
The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.
GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.
The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.