The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition. NAIP is administered by the USDA's Farm Service Agency (FSA) through the Aerial Photography Field Office in Salt Lake City. This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.

What is NAIP?The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the contiguous U.S. A primary goal of the NAIP program is to make digital ortho photography available to governmental agencies and the public within a year of acquisition.NAIP is administered by the USDA's Farm Production and Conservation Business Center through the Aerial Photography Field Office in Salt Lake City. The APFO as of August 16, 2020 has transitioned to the USDA FPAC-BC's Geospatial Enterprise Operations Branch (GEO). This "leaf-on" imagery is used as a base layer for GIS programs in FSA's County Service Centers, and is used to maintain the Common Land Unit (CLU) boundaries.How can I Access NAIP?On the web GEO (APFO) public image services can be accessed through the REST endpoint here. Compressed County Mosaics (CCMs) are available to the general public through the USDA Geospatial Data Gateway. All years of available imagery may be downloaded as 1/2, 1, or 2 meter CCMs depending on the original spatial resolution. CCMs with a file size larger than 8 GB are not able to be downloaded from the Gateway. Full resolution 4 band quarter quads (DOQQs) are available for purchase from FPAC GEO. Contact the GEO Customer Service Section for information on pricing for DOQQs and how to obtain CCMs larger than 8 GB. A NAIP image service is also available on ArcGIS Online through an organizational subscription.How can NAIP be used?NAIP is used by many non-FSA public and private sector customers for a wide variety of projects. A detailed study is available in the Qualitative and Quantitative Synopsis on NAIP Usage from 2004 -2008: Click here for a list of NAIP Information and Distribution Nodes.When is NAIP acquired?NAIP projects are contracted each year based upon available funding and the FSA imagery acquisition cycle. Beginning in 2003, NAIP was acquired on a 5-year cycle. 2008 was a transition year, a three-year cycle began in 2009, NAIP was on a two-year cycle until 2016, currently NAIP is on a 3 year refresh cycle. Click here >> for an interactive PDF status map of NAIP acquisitions from 2002 - 2018. 2021 acquisition status dashboard is available here.What are NAIP Specifications?NAIP imagery is currently acquired at 60cm ground sample distance (GSD) with a horizontal accuracy that matches within four meters of photo-identifiable ground control points.The default spectral resolution beginning in 2010 is four bands: Red, Green, Blue and Near Infrared.Contractually, every attempt will be made to comply with the specification of no more than 10% cloud cover per quarter quad tile, weather conditions permitting.All imagery is inspected for horizontal accuracy and tonal quality. Make Comments/Observations about current NAIP imagery.If you use NAIP imagery and have comments or find a problem with the imagery please use the NAIP Imagery Feedback Map to let us know what you find or how you are using NAIP imagery. Click here to access the map.**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: National Agriculture Imagery Program (NAIP) History 2002-2021Item Type: Web Mapping Application URL Summary: Story map depicting the highlights and changes throughout the National Agriculture Imagery Program (NAIP) from 2002-2021.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: URL referencing this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=445e3dfd16c4401f95f78ad5905a4cceFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8eb6c5e7adc54ec889dd6fc9cc2c14c4UID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: May 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science at large, and increasingly for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER). This project provides the first fine-resolution land-cover mapping of the CAP LTER using 2010 NAIP four-band data. It demonstrates a new object-based method capable of delivering robust outputs for the range of research activities not only undertaken by the program of study but for such studies that appear to be emerging worldwide. The classification process incorporates cadastral GIS data to assist the land-cover type extraction at the parcel scale, and a hierarchical network approach that balances computation time with classification accuracy. Decision rules that may prove useful for other high resolution image classification in other arid-land metropolitan areas are provided.

This Normalized Difference Vegetation Index (NDVI) layer features recent high-resolution (1-meter or better) aerial imagery for the continental United States, made available by the USDA Farm Production and Conservation Business Center (FPAC). The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year.This imagery layer is updated annually as new imagery is made available. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection. The imagery is published in 4-bands (Red, Green, Blue, and Near Infrared) where available. Additional NAIP renderings include Natural Color and Color Infrared. Key PropertiesGeographic Coverage: Continental United States (Hawaii and Puerto Rico available for some years)Temporal Coverage: 2010 to 2023Spatial Resolution: 0.3-meter to 1-meterRevisit Time: Typically every other yearSource Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Analysis: Optimized for analysisMultispectral Bands:BandDescriptionSpatial Resolution (m)1Red0.3 - 12Green0.3 - 13Blue0.3 - 14Near Infrared0.3 - 1 Usage Tips and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and calculations for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer is NDVI ((Red - Near Infrared) / (Red + Near Infrared)).If natural color visualization is your primary use case for NAIP, you might consider using the NAIP Imagery tile layer for optimal display performance.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent imagery available for a given area is prioritized and dynamically fused into a single mosaicked image layer. To discover and isolate specific images for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Using the "None" processing template option as input to analysis provides all bands with raw pixel values and is recommended for many use cases. Otherwise, only processing templates that include a "for analysis" designation should be used as input to analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific year, year range, state, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer. Data SourceNAIP imagery is credited to the United States Department of Agriculture (USDA) Farm Production and Conservation Business Center (FPAC). All imagery in this layer was is sourced from the NAIP Registry of Open Data on AWS.

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. This service contains NAIP imagery in the Web Mercator projection.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/imagery/rest/services/NAIP/MD_NAIPImagery/ImageServer

Detailed land-cover mapping is essential for a range of research issues addressed by sustainability science, especially for questions posed of urban areas, such as those of the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) program. This project provides a 1-meter land-cover mapping of the CAP LTER study area (greater Phoenix metropolitan area and surrounding Sonoran desert). The mapping is generated primarily using 2015 National Agriculture Imagery Program (NAIP) four-band data, with auxiliary GIS data used to improve accuracy. Auxiliary data include the 2015 cadastral parcel data, the 2014 USGS LiDAR data (1-meter), the 2014 Microsoft/OpenStreetMap Building Footprint data, the 2015 Street TIGER/Line, and a previous (2010) NAIP-based land-cover map of the study area (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=623). Among auxiliary data, building footprints and LiDAR data significantly improved the boundary detection of above-ground objects. Post-classification, manual editing was applied to minimize classification errors. As a result, the land-cover map achieves an overall accuracy of 94 per cent. The map contains eight land cover classes, including: (1) building, (2) asphalt, (3) bare soil and concrete, (4) tree and shrub, (5) grass, (6) water, (7) active cropland, and (8) fallow. When compared to the aforementioned, previous (2010) NAIP-based land-cover map for the study area, buildings and tree canopies are classified more accurately in this 2015 land-cover map.

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. This service contains NAIP imagery from 2011 in the Web Mercator projection.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Image Service Layer:https://mdgeodata.md.gov/imagery/rest/services/NAIP/NAIPImagery2011/ImageServer

This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset.

This image service is available through CTECO, a partnership between UConn CLEAR and CT DEEP. It is a virtual mosaic of GeoTIFF tiles covering the state of Connecticut.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Maps used for field sampling were created using 2013 National Agriculture Imagery Program (NAIP) aerial imagery. Higher resolution imagery was downloaded from the Clark County GIS website for the Water Resources Education Center (Clark County 2014).

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here.

Maps used for field sampling were created using 2013 National Agriculture Imagery Program (NAIP) aerial imagery. Higher resolution imagery was downloaded from the Clark County GIS website for the Water Resources Education Center (Clark County 2014).

This data layer is an element of the Oregon GIS Framework. A mosaic derived from half-meter resolution color Digital Orthophoto Quadrangles (DOQ) of the entire state of Oregon from the summer of 2005 for multiple state agencies in Oregon. The original content was produced utilizing the scanned aerial film acquired during peak agriculture growing seasons under the National Agriculture Imagery Program (NAIP) under contract for the United States Department of Agriculture (USDA) for the Farm Service Agency's (FSA) Compliance Program. A DOQ is a raster image in which displacement in the image caused by sensor orientation and terrain relief has been removed. A DOQ combines the image characteristics of a photograph with the geometric qualities of a map. The geographic extent of the DOQ is a full 7.5-minute map (latitude and longitude) with a nominal buffer. The horizontal accuracy is within 5 meters of reference ortho imagery (1992 USGS DOQs.) The 1992 USGS DOQ imagery met National Map Accuracy Standards at 1:24,000 scale for 7.5-minute quadrangles. Translated to the ground, the 0.5 mm error distance at 1:24,000 scale is 39.4 ft (12 meters), making the absolute accuracy for the 2005 Oregon DOQs +/- 17 meters. The process of reprojecting and mosaicing the images may have added a potential shift of +/-0.75 meters, making the cumulative accuracy +/-17.75 meters. The original images were projected into Oregon Lambert (2992). The imagery is provided in the Web Mercator Auxiliary Sphere projection as a tiled service, and in the State Lambert projection as an image service. Using web services to stream imagery: https://imagery.oregonexplorer.info/arcgis/rest/services/NAIP_2005

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. To complete the automated phase, CTI subcontracted with Photo Science (based in Lexington, KY) to create a BIBE landform layer and a drainage/wash layer. Photo Science reviewed and acquired all National Elevation Dataset (NED) 10-meter DEMs for the project area and mosaiced them into a seamless coverage. The DEM data was then manipulated to create the following derived spatial layers: aspect, slope, three hillshade datasets (different azimuth angles), a contour range layer, and a compound topographic index (or wetness index) that models water flow and accumulation. Similarly, Photo Science also acquired the 2012 National Agriculture Imagery Program (NAIP) imagery for the entire project area as high-resolution (1-meter pixels) digital ortho quarter quadrangles (DOQQs). The NAIP DOQQs were mosaiced and resampled from 1-meter to 10-meter pixels to match the DEM resolution. Erdas Imagine software was then used to derive a normalized difference vegetation index (NDVI) and a near infrared (NIR) band texture layer from the imagery using a 9x9 moving window. During the planning and coordination phase, CTI staff reviewed all available digital imagery for its potential use as the BIBE basemap. The most promising and easy to access was the data catalog found on the Texas Natural Resource Information System (TNRIS) website. Navigating to the orthoimagery-statewide web page, the list of existing imagery covering BIBE included multiple NAIP products. The corresponding 2010 and 2012 NAIP 1-meter DOQQs for BIBE were downloaded and used during the early planning stages of this project and to produce field maps and interim products.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. Base Imagery acquired from the USDA FSA Aerial Photography Field Office acquired through the National Agriculture Imagery Program: 2005 - 1:12,000-scale true color ortho-rectified imagery, compressed county mosaic,2 meter pixel resolution. Ancillary Imagery acquired by the Kansas Applied Remote Sensing Program, a division of the Kansas Biological Survey: October 26, 2005 - 1:8,500-scale color infrared digital ortho-imagery, uncompressed, 0.75 meter pixel resolution. A combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office

This is an ArcGIS Server Image Service of the 4-band 2021 National Agricultural Imagery Program (NAIP) orthorectified digital aerial photos of Montana. Imagery defaults to natural color. To view the imagery as false-color infrared (CIR), select band 4 as the red image, band 1 as the green, and band 2 as the blue. This data set contains imagery from the National Agriculture Imagery Program (NAIP). These data are digital aerial photos, at 60 centimeter resolution, of the state of Montana, taken in 2021. The data are available from the State Library in two different formats. The most accessible format is a downloadable collection of compressed county mosaic (CCM) 4-Band MrSID images. These data are in UTM coordinates. The FTP folder containing these images is https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2023_NAIP/UTM_County_Mosaics The data are available from the State Library as a collection 10,505 4-band (near infrared, red, green and blue) TIFF images in UTM coordinates. Each image is about 425 megabytes. The tiling format of the TIFF imagery is based on 3.75 x 3.75 minute quarter-quadrangles with a 300 pixel buffer on all four sides. An ESRI shapefile index showing the extent and acquisition dates of the TIF images is available at:Tile Index: https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2023_NAIP/NAIP2023_TileIndex_shp.zipPhoto Dates: https://ftpgeoinfo.msl.mt.gov/Data/Spatial/MSDI/Imagery/2023_NAIP/NAIP2023_ImageDates_shp.zipTo order TIFF images from the State Library, select the quadrangles you want from the tiff index shapefile and send them to the Library, along with a storage device of sufficient size to hold them and return postage for the device. More information on ordering can be found at the following website https://msl.mt.gov/geoinfo/data/Aerial_Photos/Ordering

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.

OverviewThis dataset contains real overhead images of wind turbines in the US collected through the National Agriculture Imagery Plan (NAIP), as well as synthetic overhead images of wind turbines created to be similar to the real images. All of these images are 608x608. For more details on the methodology and data, please read the sections below, or look at our website: Locating Energy Infrastructure with Deep Learning (duke-bc-dl-for-energy-infrastructure.github.io). Real DataThe real data consists of images.zip and labels.zip. There are 1,742 images in images.zip, and for each image in this folder, there is a corresponding label with the same name, but a different extension. Some images do not have labels, meaning there are no wind turbines in those images. Many of these overhead images of wind turbines were collected from Power Plant Satellite Imagery Dataset (figshare.com) and then hand labeled. Others were collected using Google Earth Engine or EarthOnDemand and then labeled. All of the images collected are from the National Agricultural Imagery Program (NAIP), and all are 608x608 pixels. The labels are in YOLOv3 format, meaning each line in the text file corresponds with one wind turbine. Each line is formatted as: class x_center y_center width height. Since there is only one class, class is always zero, and the x, y, width, and height are relative to the size of the image and are between 0-1. The image_locations.csv file contains the latitude and longitude for each image. It also contains the image's geographic domain that we defined. Our data comes from what we defined as four regions - Northeast (NE), Eastern Midwest (EM), Northwest (NW), and Southwest (SW), and these are included in the csv file for each image. These regions are defined by groups of states, so any data in WA, ID, or MT would be in the Northwest region. Synthetic DataThe synthetic data consists of synthetic_images.zip and synthetic_labels.zip. These images and labels were automatically generated using CityEngine. Again, all images are 608x608, and the format of the labels is the same. There are 943 images total, and at least 200 images for each of the four geographic domains that we defined in the US (Northwest, Southwest, Eastern Midwest, Northeast). The generation of these images consisted of the software selecting a background image, then generating 3D models of turbines on top of that background image, and then positioning a simulated camera overhead to capture an image. The background images were collected nearby the locations of the testing images. ExperimentationOur Duke Bass Connections 2020-2021 team performed many experiments using this data to test if the synthetic imagery could help the performance of our object detection model. We designed experiments where we would have a baseline dataset of just real imagery, train and test an object detection model on it, and then add in synthetic imagery into the dataset, train the object detection model on the new dataset, and then compare it's performance with the baseline. For more information on the experiments and methodology, please visit our website here: Locating Energy Infrastructure with Deep Learning (duke-bc-dl-for-energy-infrastructure.github.io).

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of res ...

Digitization of oil and gas well pad sites in the Piceance region of Western Colorado. Well pad sites were delineated using a modified version of the Rapid Land Cover Mapping protocol (Preston and Kim, 2016). The base imagery used to delineate boundaries is the 2015 National Agriculture Imagery Program (NAIP) imagery. Well coordinate locations facilitating the targeting of well pad sites were downloaded from the Colorado Oil and Gas Conservation Commission (COGCC) in February 2016.

The NAIP Imagery Hybrid (US Edition) web map features recent high-resolution National Agriculture Imagery Program (NAIP) imagery for the United States and is optimized for display quality and performance. The map also includes a reference layer. This NAIP imagery is from the USDA Farm Services Agency. The NAIP imagery in this map has been visually enhanced and published as a raster tile layer for optimal display performance.NAIP imagery collection occurs on an annual basis during the agricultural growing season in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection.This basemap is available in the United States Vector Basemaps gallery and uses NAIP Imagery and World Imagery (Firefly) raster tile layers. It also uses the Hybrid Reference (US Edition) and Dark Gray Base (US Edition) vector tile layers.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.