Users can browse the map interactively or search by lot ID or address. Available basemaps include aerial images, topographic contours, roads, town landmarks, conserved lands, and individual property boundaries. Overlays display landuse, zoning, flood, water resources, and soil characteristics in relation to neighborhoods or parcels. Integration with Google Street View offers enhanced views of the 2D map location. Other functionality includes map markup, printing, viewing the property record card, and links to official tax maps where available.NRPC's implementation of MapGeo dates back to 2013, however it is the decades of foundational GIS data development at NRPC and partner agencies that has enabled its success. NRPC refreshes the assessing data yearly; the map data is maintained in an ongoing manner.

This data provides the integrated cadastral framework for Canada Lands. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), registration plans (RS) and location sketches (LS) archived in the Canada Lands Survey Records.

Auburn Maine's parcel Inquiry map with optional zoning and high-resolution aerial photography. Optional zoning layers. Map provides detailed assessing data for each parcel as well as links to WebPro assessing records and Google Street View. Users can search for parcels using parcel ID, location, or owner name. Advanced search options provide ability to select and buffer parcels with an optional export to csv file.

The Unpublished Digital Geologic-GIS Map of Virgin Islands National Park, Virgin Islands is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (viis_geology.gdb), a 10.1 ArcMap (.mxd) map document (viis_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (viis_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (viis_geology_gis_readme.pdf). Please read the viis_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). 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 (viis_geology_metadata.txt or viis_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 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). The GIS data projection is NAD83, UTM Zone 20N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Virgin Islands National Park.

The Unpublished Digital Geologic-GIS Map of Mount Desert Island and Vicinity, Acadia National Park, Maine is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (acad_geology.gdb), a 10.1 ArcMap (.mxd) map document (acad_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (acad_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (acad_geology_gis_readme.pdf). Please read the acad_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Maine 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 (acad_geology_metadata.txt or acad_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 1:50,000 and United States National Map Accuracy Standards features are within (horizontally) 25.4 meters or 83.3 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 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). The GIS data projection is NAD83, UTM Zone 19N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Acadia National Park.

The Maine Geoparcel Viewer Application allows users to search and view available digital parcel data for Organized Townships and Unorganized Territories in the State of Maine. The Maine GeoLibrary and the Maine Office of GIS do not maintain parcel data for communities, cannot verify parcel ownership, and are not responsible for individual parcel data verification or updating emergency records concerning parcel addresses. If you have questions about a specific parcel, please contact the appropriate Town Office or County Registry of Deeds for the most up-to-date information.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. The "Maine Parcels Organized Towns Feature" layer and "Maine Parcels Organized Towns ADB" table are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Please contact the appropriate Town Office or the County Registry of Deeds for more up-to-date parcel information. Organized Town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), Maine Revenue Services is the authoritative source for parcel data. The "Maine Parcels Unorganized Territory" layer is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services to compile up-to-date parcel ownership information.

NZ Parcel Boundaries Wireframe provides a map of land, road and other parcel boundaries, and is especially useful for displaying property boundaries.
This map service is for visualisation purposes only and is not intended for download. You can download the full parcels data from the NZ Parcels dataset.
This map service provides a dark outline and transparent fill, making it perfect for overlaying on our basemaps or any map service you choose.
Data for this map service is sourced from the NZ Parcels dataset which is updated weekly with authoritative data direct from LINZ’s Survey and Title system. Refer to the NZ Parcel layer for detailed metadata.
To simplify the visualisation of this data, the map service filters the data from the NZ Parcels layer to display parcels with a status of 'current' only.
This map service has been designed to be integrated into GIS, web and mobile applications via LINZ’s WMTS and XYZ tile services. View the Services tab to access these services.
See the LINZ website for service specifications and help using WMTS and XYZ tile services and more information about this service.

Approximate boundaries for all land parcels in New Brunswick. The boundaries are structured as Polygons. The Property Identifier number or PID is included for each parcel.

Site address points represent the location of site or service delivery addresses assigned by local governments.This data was initially created in 2017. The process involved using county Parcel data, field verification, commercial location PDFs provided by Public Safety, Community Development subdivision final maps, Google Street View, and Google Maps. The data continues to be updated by GIS and all new entries are input by Community Development.

There are approximately 2.6 million agricultural land parcels in England, and the Parcel Points (England) point dataset represents the approximate centres, or centroids of these parcels. The number of agricultural parcels varies over time as fields are split or joined consequently creating new fields with new centroids. Some parcels may be sold for development and are no longer available for agricultural use, occasionally land that was previously developed may return to agriculture, e.g. disused airfields.

Parcel Points is a simple point dataset that uses the centres of agricultural parcels attributed only with the parcel reference ID. Parcel points are categorised according to county and are available as England-wide coverage too.

Users can use the data as supplied or attach their own attributes. When viewed overlying other publicly available data, e.g. satellite imagery (Google earth), OS open data, OpenStreetMap, etc. that may feature agricultural land the points provide a useful reference point to identify the approximate centre of agricultural parcels.

Parcel Points uses the Open Government Licence v3.0 as used by other publicly accessible data on the Defra Data Services Platform and will be updated every 6 months.

Coverage will be for agricultural land parcels in England only.

Users should be aware that client-side constraints associated WFS and OGC API, such as maximum feature limits, may affect the loading and display of parcel points. Parcel Points WMS are not affected to the same extent.

Below is a quick rundown of the tools available in the web map!The first new thing you may notice is the ability to search from in the splash window that appears. This hopefully reduces the number of clicks people will need to get to their information. There's the same search bar in the upper left once you click out of the splash screen.The Query tool has existed in this form on the sub-maps, but now it is here with all the layers. I want to highlight "Search by Legal Description" as a nifty way to find parcels associated with a specific subdivision. I also want to highlight the "find tax parcels/addresses within specified distance" queries. Those let you select every tax parcel or address within a feature you draw (a point, line, or polygon). This is good for finding what properties within a distance need to be notified of something. That can then be exported as an Excel table (csv). This can also help you identify whether something falls within certain setbacks.The Basemaps is the same as it was before. I haven't gotten the Virginia Geographic Information Network imagery from 2017 and 2021 to successfully appear here, but you can find that in the map layers at the bottom.We have a lot of data layers! I currently have the default as every group expanded out, so you can scroll and see all the layers, but you can go through and click to collapse any groups you don't want expanded. Okay, the select tool is super cool, and lets you really dive into some fun GIS attribute querying! As an example, you can select all the FEMA Flood Zones that are AO, then select all the tax parcels that are affected by (intersect) those AO zones! These results can also be exported into an Excel table. A great deal of GIS analysis is possible just using Select by Attributes and Select by Location, so this tool really ramps up the power of the web map so it can do some of what the desktop GIS software can do!Continuing our tour of the tools, we come to the coordinates tool. This one also existed already in the sub-maps, but is now with all the layers. Unfortunately, the tool is a little annoying, and won't retain my defaults. You have to click the little plus sign target thing, then you can click on the map to get the coordinates. The coordinate system defaults to WGS 1984 Web Mercator (the same thing Google Maps uses), but much of our data uses NAD 1983 State Plane Virginia South, so you can click the dropdown arrow to the right to select either one. Exciting news related to this: in 2026 they are releasing the new coordinate system on which they've been working! It should make the data in GIS more closely align with features in reality, but you will not need to change any of the ways you interact with the data.The next tool is the Elevation Profile tool. It's very nifty! You can draw a profile to see how the elevation changes, and as you move your cursor along the graph, it shows where along your transect you are! It helps explain some of the floodplain and sea level rise boundaries.You know the measure tool well, but this one retains the defaults in feet and acres, which is very exciting! No more having to change the units every time you want to measure (unless you want other than feet and acres).The draw tool is our penultimate stop on the tour! It is largely the same as what currently exists on the public web map, so I shan't delve into it here. When you draw a feature now though, it appears in the layers tab (until you close the map), which can let you toggle the drawing on and off to work with what is beneath it. It can help as you plan in where you might want to put new constructions.The print tool is also largely the same, but I've been finding the tool in this new Experience Builder format is less buggy than the one in the retiring Web App Builder that made the current Public Web Map.

We developed a map of cropland and grassland allocation for Switzerland based on several indices dominantly derived from Sentinel-2 satellite imagery captured over multiple growing seasons. The classification model was trained based on parcel-based data derived from landholder reporting. The mapping was conducted on Google Earth Engine platform using random forest classifier. Areas of high vegetation, shrubland, sealed surface and non-vegetated areas were masked out from the country-wide map. The resulting map has high accuracy in lowlands as well as mountainous areas.

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

This data represents a land use survey of 2017 San Joaquin County conducted by the California Department of Water Resources, North Central Region Office staff. Land use field boundaries were digitized with ArcGIS 10.5.1 using 2016 NAIP as the base, and Google Earth and Sentinel-2 imagery website were used as reference as well. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were not drawn to represent legal parcel (ownership) boundaries and are not meant to be used as parcel boundaries. The field work for this survey was conducted from July 2017 through August 2017. Images, land use boundaries and ESRI ArcMap software were loaded onto Surface Pro tablet PCs that were used as the field data collection tools. Staff took these Surface Pro tablet into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Agricultural fields the staff were unable to access were designated 'E' in the Class field for Entry Denied in accordance with the 2016 Land Use Legend. The areas designated with 'E' were also interpreted using a combination of Google Earth, Sentinel-2 Imagery website, Land IQ (LIQ) 2017 Delta Survey, and the county of San Joaquin 2017 Agriculture GIS feature class. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Water source information was not collected for this land use survey. Therefore, the water source has been designated as Unknown. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region,Office and at DRA's headquarters office under the leadership of Muffet Wilkerson, 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.

ParcelMap BC is the current, complete and trusted mapped representation of titled and Crown land parcels across British Columbia, considered to be the point of truth for the graphical representation of property boundaries. It is not the authoritative source for the legal property boundary or related records attributes; this will always be the plan of survey or the related registry information. This particular dataset is a subset of the complete ParcelMap BC data and is comprised of the parcel fabric and attributes for over two million parcels published under the Open Government Licence - British Columbia. Notes: 1. Parcel title information is sourced from the BC Land Title Register. Title questions should be directed to a local Land Title Office. 2. This dataset replaces the Integrated Cadastral Fabric.

The Land Cover dataset demarcates 14 land cover types by area; such as Residential, Commercial, Industrial, Forest, Agriculture, etc. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Geography Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 1994 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: The dataset was created by Chester Environmental through combined image processing and GIS analysis of Landsat TM imagery of October 2, 1992, existing aerial photography, hardcopy and digital mapping sources and Census Bureau demographic data. The original dataset was created in 1993, then updated by Chester in 1994. Other: none Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/1VfUflfki42mpLSkr1R-up_OXGD3mHnv8tqeXf6XS9O0/edit?usp=sharing) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.

License

Creative Commons CC-BY

Disclaimer

Despite our best efforts to validate the data, some information may be incorrect.

Description of the dataset

Typologies of UA

Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.

Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.

Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.

Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).

Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.

Nurseries: A large area subdivided into multiple plots managed for growing ornamental plants and flowers.

Land use typologies

Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).

Vineyard: grape vines grown in order to produce wine or table grape.

Olive groves: olive trees grown in order to produce olive oil or table olives.

Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.

Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.

Nurseries: ornamental plants, trees, flowers.

Credit

Pulighe G., Lupia F. (2019) Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. Sustainability 2019, 11(7), 1846

https://www.mdpi.com/2071-1050/11/7/1846

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 2008 Yolo 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 was mapped by staff of DWR’s North Central Region using 2006 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter resolution digital imagery, and the Google Earth website. 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 San Francisco County conducted by the California Department of Water Resources, North Central Region Office staff. Land use field boundaries were digitized with ArcGIS9.3 using 2006 NAIP imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. Yolo County contains only a few, small agricultural areas, one bison pasture in Golden Gate Park, and some community gardens. The land use was entirely photo interpreted using NAIP imagery and Google Earth. Sources of irrigation water were not identified. 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.

Land-Use Data Quality ControlEvery 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 2014 San Francisco 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 was mapped by staff of DWR’s North Central Region using 2014 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter resolution digital imagery, and the Google Earth website. 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 San Francisco County conducted by the California Department of Water Resources, North Central Region Office staff. Land use field boundaries were digitized with ArcGIS10.2 using 2012 and 2014 NAIP imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. San Francisco County contains only a few, small agricultural areas, one bison pasture in Golden Gate Park, and some community gardens. The land use was entirely photo interpreted using NAIP imagery and Google Earth. Sources of irrigation water were not identified. 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.