Purpose: This is an ArcGIS Pro template that GIS Specialists can use to identify vulnerable populations and special needs infrastructure most at risk to flooding events.How does it work?Determine and understand the Place Vulnerability (based on Cutter et al. 1997) and the Special Needs Infrastructure for an area of interest based on Special Flood Hazard Zones, Social Vulnerability Index, and the distribution of its Population and Housing units. The final product will be charts of the data distribution and a Hosted Feature Layer. See this Story Map example for a more detailed explanation.This uses the FEMA National Flood Hazard Layer as an input (although you can substitute your own flood hazard data), check availability for your County before beginning the Task: FEMA NFHL ViewerThe solution consists of several tasks that allow you to:Select an area of interest for your Place Vulnerability Analysis. Select a Hazard that may occur within your area of interest.Select the Social Vulnerability Index (SVI) features contained within your area of interest using the CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI layer at the census tract level in the map.Determine and understand the Social Vulnerability Index for the hazard zones identified within you area of interest.Identify the Special Needs Infrastructure features located within the hazard zones identified within you area of interest.Share your data to ArcGIS Online as a Hosted Feature Layer.FIRST STEPS:Create a folder C:\GIS\ if you do not already have this folder created. (This is a suggested step as the ArcGIS Pro Tasks does not appear to keep relative paths)Download the ZIP file.Extract the ZIP file and save it to the C:\GIS\ location on your computer. Open the PlaceVulnerabilityAnalysis.aprx file.Once the Project file (.aprx) opens, we suggest the following setup to easily view the Tasks instructions, the Map and its Contents, and the Databases (.gdb) from the Catalog pane.The following public web map is included as a Template in the ArcGIS Pro solution file: Place Vulnerability Template Web MapNote 1:As this is a beta version, please take note of some pain points:Data input and output locations may need to be manually populated from the related workspaces (.gdb) or the tools may fail to run. Make sure to unzip/extract the file to the C:\GIS\ location on your computer to avoid issues.Switching from one step to the next may not be totally seamless yet.If you are experiencing any issues with the Flood Hazard Zones service provided, or if the data is not available for your area of interest, you can also download your Flood Hazard Zones data from the FEMA Flood Map Service Center. In the search, use the FEMA ID. Once downloaded, save the data in your project folder and use it as an input.Note 2:In this task, the default hazard being used are the National Flood Hazard Zones. If you would like to use a different hazard, you will need to add the new hazard layer to the map and update all query expressions accordingly.For questions, bug reports, or new requirements contact pdoherty@publicsafetygis.org

This map data layer represents the GIS Map Panel Boundaries for the City of Bloomington, Indiana. The GIS Map Panel Boundaries data layer was created as a reference grid for the GIS map data. The grid tiles are 3000' by 2000' and cover a total of 86.3 square miles of central Monroe County in Indiana. The panel tiles are located arbitrary to any geographic features

The S_LOMR feature class should contain at least one record for each Letter of Map Revision incorporated into the NFHL. Multipart polygons are not allowed. The spatial entities representing LOMRs are polygons. The spatial information contains the bounding polygon for each LOMR area, broken on panel boundaries.Technical Reference - http://www.fema.gov/media-library-data/1449862521789-e97ed4c7b7405faa7c3691603137ec40/FIRM_Database_Technical_Reference_Nov_2015.pdfFlood hazard and supporting data are developed using specifications for horizontal control consistent with 1:12,000–scale mapping. If you plan to display maps from the National Flood Hazard Layer with other map data for official purposes, ensure that the other information meets FEMA’s standards for map accuracy. The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. USGS imagery and map services that meet this standard can be found by visiting the Knowledge Sharing Site (KSS) for Base Map Standards (420). Other base map standards can be found at https://riskmapportal.msc.fema.gov/kss/MapChanges/default.aspx. You will need a username and password to access this information.The NFHL data are from FEMA’s Flood Insurance Rate Map (FIRM) databases. New data are added continually. The NFHL also contains map changes to FIRM data made by Letters of Map Revision (LOMRs). The NFHL is stored in North American Datum of 1983, Geodetic Reference System 80 coordinate system, though many of the NFHL GIS web services support the Web Mercator Sphere projection commonly used in web mapping applications.

Polygon geometry with attributes displaying the Federal Emergency Management Agency Flood Insurance Rate Map panels in East Baton Rouge Parish, Louisiana.

Sometimes a basic solid color for your map's labels and text just isn't going to cut it. Here is an ArcGIS Pro style with light and dark gradient fills and shadow/glow effects that you can apply to map text via the "Text fill symbol" picker in your label pane. Level up those labels! Make them look touchable. Glassy. Shady. Intriguing.Find a how-to here.Save this style, add it to your ArcGIS Pro project, then use it for any text (including labels).**UPDATE**I've added a symbol that makes text look like is being illuminated from below, casting a shadow upwards and behind. Pretty dramatic if you ask me. Here is an example:Happy Mapping! John Nelson

The July 2025 issue of the GIS Office Newsletter features updates about the GIS Office, a summary of the most recent GIS Advisory Council meeting, GIS resources, and more.

Solar power is environment-friendly and is being promoted by government agencies and power distribution companies. Government agencies can use solar panel detection to offer incentives such as tax exemptions and credits to residents who have installed solar panels. Policymakers can use it to gauge adoption and frame schemes to spread awareness and promote solar power utilization in areas that lack its use. This information can also serve as an input to solar panel installation and utility companies and help redirect their marketing efforts.

Traditional ways of obtaining information on solar panel installation, such as surveys and on-site visits, are time-consuming and error-prone. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of solar panel detection, reducing time and effort required significantly.



Using the model



Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.



Fine-tuning the model



This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.



Input



High-resolution (5–15 cm) RGB imagery.



Output



Feature class containing detected solar panels.



Applicable geographies



The model is expected to work well in the United States.



Model architecture



The model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.



Accuracy metrics



The model has an average precision score of 0.764.

Training dataThis model has been trained on an Esri proprietary solar panel detection dataset. Sample results

Here are a few results from the model.

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy.Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection.For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.caAvailable Products:ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServerhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022Central Ontario Orthophotography Project (COOP) 2021South-Western Ontario Orthophotography Project (SWOOP) 2020Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020South Central Ontario Orthophotography Project (SCOOP) 2018North-Western Ontario Orthophotography Project (NWOOP) 2017Central Ontario Orthophotography Project (COOP) 2016South-Western Ontario Orthophotography Project (SWOOP) 2015Algonquin Orthophotography Project (2015)Additional Documentation:Ontario Web Raster Services User Guide (Word)Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every yearContact:Geospatial Ontario (GEO), geospatial@ontario.ca

Location and attributes for FIRM hardcopy map panels. The spatial entities representing FIRM panels are polygons. The polygon for the FIRM panel corresponds to the panel neatlines. FIRM panels must not overlap or have gaps within a study. In situations where a portion of a panel lies outside the jurisdiction being mapped, the user must refer to the S_Pol_Ar table to determine the portion of the panel area where the FIRM Database shows the effective flood hazard data for the mapped jurisdiction.

This is a fine-tuned model for New Zealand, derived from a pre-trained model from Esri. It has been trained using LINZ aerial imagery (0.075 m spatial resolution) for Wellington You can see its output in this app https://niwa.maps.arcgis.com/home/item.html?id=1ca4ee42a7f44f02a2adcf198bc4b539Solar power is environment friendly and is being promoted by government agencies and power distribution companies. Government agencies can use solar panel detection to offer incentives such as tax exemptions and credits to residents who have installed solar panels. Policymakers can use it to gauge adoption and frame schemes to spread awareness and promote solar power utilization in areas that lack its use. This information can also serve as an input to solar panel installation and utility companies and help redirect their marketing efforts.Traditional ways of obtaining information on solar panel installation, such as surveys and on-site visits, are time consuming and error-prone. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of solar panel detection, reducing time and effort required significantly.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS Proor ArcGIS Enterprise – ArcGIS Image Server with Raster Analytics configuredor ArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the Esri guide to using their USA Solar Panel detection model (https://www.arcgis.com/home/item.html?id=c2508d72f2614104bfcfd5ccf1429284). Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution (5-15 cm) RGB imageryOutputFeature class containing detected solar panelsApplicable geographiesThe model is expected to work well in New ZealandModel architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.9244444449742635NOTE: Use at your own risk_Item Page Created: 2022-02-09 02:24 Item Page Last Modified: 2025-04-05 16:30Owner: NIWA_OpenData

Connecticut Railroads is a 1:24,000-scale, feature-based layer that includes railroad features on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. The layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 and does not represent the railroad system in Connecticut at any one particular point in time. The layer does not depict current conditions and excludes many railroads that have been built, modified, or removed since the time these topographic quadrangle maps were published. The layer includes railroad tracks, bridges, drawbridges, roundhouses, sidings, tracks, tunnels, underpasses, and stations. It does not include train schedule or track related information. Features are linear and represent railroad track centerlines. Attribute information is comprised of codes to cartographically represent (symbolize) rail features on a map. This layer was originally published in 1994. The 2005 edition includes the same rail features published in 1994, but the attribute information has been slightly modified and made easier to use.

This New Zealand solar panel detection Deep Learning Package can detect solar panels from high resolution imagery. This model is trained on high resolution imagery from New Zealand.Solar power is environmentally friendly and is being promoted by government agencies and power distribution companies. Government agencies can use solar panel detection to offer incentives such as tax exemptions and credits to residents who have installed solar panels. Policymakers can use it to gauge adoption and frame schemes to spread awareness and promote solar power utilization in areas that lack its use. This information can also serve as an input to solar panel installation and utility companies and help redirect their marketing efforts.Traditional ways of obtaining information on solar panel installation, such as surveys and on-site visits, are time consuming and error-prone. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of solar panel detection, reducing time and effort required significantly.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.When using the Detect Objects using Deep Learning geoprocessing tool, ticking the Non Maximum Suppression box is recommended, for reference a Max Overlap Ratio of 0.3 was used for the example images below. Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution (7.5 cm) RGB imagery.OutputFeature class containing detected solar panels.Applicable geographiesThe model is expected to work well in New Zealand.Model architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.83.Sample resultsSome results from the model are displayed below: To learn how to use this model, see this story

The ArcGIS Online US Geological Survey (USGS) topographic map collection now contains over 177,000 historical quadrangle maps dating from 1882 to 2006. The USGS Historical Topographic Map Explorer app brings these maps to life through an interface that guides users through the steps for exploring the map collection:Find a location of interest.View the maps.Compare the maps.Download and share the maps or open them in ArcGIS Desktop (ArcGIS Pro or ArcMap) where places will appear in their correct geographic location. Save the maps in an ArcGIS Online web map.

Finding the maps of interest is simple. Users can see a footprint of the map in the map view before they decide to add it to the display, and thumbnails of the maps are shown in pop-ups on the timeline. The timeline also helps users find maps because they can zoom and pan, and maps at select scales can be turned on or off by using the legend boxes to the left of the timeline. Once maps have been added to the display, users can reorder them by dragging them. Users can also download maps as zipped GeoTIFF images. Users can also share the current state of the app through a hyperlink or social media. This ArcWatch article guides you through each of these steps: https://www.esri.com/esri-news/arcwatch/1014/envisioning-the-past.Once signed in, users can create a web map with the current map view and any maps they have selected. The web map will open in ArcGIS Online. The title of the web map will be the same as the top map on the side panel of the app. All historical maps that were selected in the app will appear in the Contents section of the web map with the earliest at the top and the latest at the bottom. Turning the historical maps on and off or setting the transparency on the layers allows users to compare the historical maps over time. Also, the web map can be opened in ArcGIS Desktop (ArcGIS Pro or ArcMap) and used for exploration or data capture.Users can find out more about the USGS topograhic map collection and the app by clicking on the information button at the upper right. This opens a pop-up with information about the maps and app. The pop-up includes a useful link to a USGS web page that provides access to documents with keys explaining the symbols on historic and current USGS topographic maps. The pop-up also has a link to send Esri questions or comments about the map collection or the app.We have shared the updated app on GitHub, so users can download it and configure it to work with their own map collections.

REQUIRED: A brief narrative summary of the data set.

View of Channelization data represents roadway paint lines, curbs, and other markings that delineate traffic lanes, bike routes, bus zones, etc. which are critical for public safety.

See full Data Guide here. Lake Bathymetry describes the water depth for selected reservoirs, lakes, ponds, and coves in Connecticut. It includes depth contours, also called bathymetric contours, that define lines of equal water depth in feet. This information was collected and compiled by the State of Connecticut, Department of Environmental Protection over a period of time using a variety of different techniques and equipment including manual depth soundings, use of an electronic depth sounder in conjunction with a GPS receiver to locate the boat, and digitizing previously published bathymetry maps. Data is compiled at a variety of scales and resolutions, depending on the collection method used for a particular waterbody. A list of the waterbodies included in this layer can be viewed in the GIS Metadata for Lake Bathymetry. This information was used to publish bathymetric maps in A Fisheries Guide to Lakes and Ponds of Connecticut, Robert P. Jacobs, Eileen B. O'Donnell, and William B. Gerrish, Connecticut Department of Environmental Protection Bulletin 35, 2002, SBN 0-942085-11-6.

The S_FIRM_Pan table contains information about the FIRM panel area. A spatial file with location information also corresponds with this data table. The spatial entities representing FIRM panels are polygons. The polygon for the FIRM panel corresponds to the panel neatlines. Panel boundaries are generally derived from USGS DOQQ boundaries. As a result, the panels are generally rectangular. In situations where a portion of a panel lies outside the jurisdiction being mapped, the user must refer to the S_Pol_Ar table to determine the portion of the panel area where the FIRM Database shows the effective flood hazard data for the mapped jurisdiction. This information is needed for the FIRM Panel Index and the following tables in the FIS report: Listing of NFIP Jurisdictions, Levees, Incorporated Letters of Map Change, and Coastal Barrier Resources System Information. The spatial entities representing FIRM panels are polygons. The polygon for the FIRM panel corresponds to the panel neatlines. Panel boundaries are generally derived from USGS DOQQ boundaries. As a result, the panels are generally rectangular. FIRM panels must not overlap or have gaps within a study. In situations where a portion of a panel lies outside the jurisdiction being mapped, the user must refer to the S_Pol_Ar table to determine the portion of the panel area where the FIRM Database shows the effective flood hazard data for the mapped jurisdiction. This information is needed for the FIRM Panel Index and the following tables in the FIS report: Listing of NFIP Jurisdictions, Levees, Incorporated Letters of Map Change, and Coastal Barrier Resources System Information.Flood hazard and supporting data are developed using specifications for horizontal control consistent with 1:12,000–scale mapping. If you plan to display maps from the National Flood Hazard Layer with other map data for official purposes, ensure that the other information meets FEMA’s standards for map accuracy. The minimum horizontal positional accuracy for base map hydrographic and transportation features used with the NFHL is the NSSDA radial accuracy of 38 feet. USGS imagery and map services that meet this standard can be found by visiting the Knowledge Sharing Site (KSS) for Base Map Standards (420). Other base map standards can be found at https://riskmapportal.msc.fema.gov/kss/MapChanges/default.aspx. You will need a username and password to access this information.The NFHL data are from FEMA’s Flood Insurance Rate Map (FIRM) databases. New data are added continually. The NFHL also contains map changes to FIRM data made by Letters of Map Revision (LOMRs). The NFHL is stored in North American Datum of 1983, Geodetic Reference System 80 coordinate system, though many of the NFHL GIS web services support the Web Mercator Sphere projection commonly used in web mapping applications.

In 2012, the CPUC ordered the development of a statewide map that is designed specifically for the purpose of identifying areas where there is an increased risk for utility associated wildfires. The development of the CPUC -sponsored fire-threat map, herein "CPUC Fire-Threat Map," started in R.08-11-005 and continued in R.15-05-006.

A multistep process was used to develop the statewide CPUC Fire-Threat Map. The first step was to develop Fire Map 1 (FM 1), an agnostic map which depicts areas of California where there is an elevated hazard for the ignition and rapid spread of powerline fires due to strong winds, abundant dry vegetation, and other environmental conditions. These are the environmental conditions associated with the catastrophic powerline fires that burned 334 square miles of Southern California in October 2007. FM 1 was developed by CAL FIRE and adopted by the CPUC in Decision 16-05-036.

FM 1 served as the foundation for the development of the final CPUC Fire-Threat Map. The CPUC Fire-Threat Map delineates, in part, the boundaries of a new High Fire-Threat District (HFTD) where utility infrastructure and operations will be subject to stricter fire‑safety regulations. Importantly, the CPUC Fire-Threat Map (1) incorporates the fire hazards associated with historical powerline wildfires besides the October 2007 fires in Southern California (e.g., the Butte Fire that burned 71,000 acres in Amador and Calaveras Counties in September 2015), and (2) ranks fire-threat areas based on the risks that utility-associated wildfires pose to people and property.

Primary responsibility for the development of the CPUC Fire-Threat Map was delegated to a group of utility mapping experts known as the Peer Development Panel (PDP), with oversight from a team of independent experts known as the Independent Review Team (IRT). The members of the IRT were selected by CAL FIRE and CAL FIRE served as the Chair of the IRT. The development of CPUC Fire-Threat Map includes input from many stakeholders, including investor-owned and publicly owned electric utilities, communications infrastructure providers, public interest groups, and local public safety agencies.

The PDP served a draft statewide CPUC Fire-Threat Map on July 31, 2017, which was subsequently reviewed by the IRT. On October 2 and October 5, 2017, the PDP filed an Initial CPUC Fire-Threat Map that reflected the results of the IRT's review through September 25, 2017. The final IRT-approved CPUC Fire-Threat Map was filed on November 17, 2017. On November 21, 2017, SED filed on behalf of the IRT a summary report detailing the production of the CPUC Fire-Threat Map(referenced at the time as Fire Map 2). Interested parties were provided opportunity to submit alternate maps, written comments on the IRT-approved map and alternate maps (if any), and motions for Evidentiary Hearings. No motions for Evidentiary Hearings or alternate map proposals were received. As such, on January 19, 2018 the CPUC adopted, via Safety and Enforcement Division's (SED) disposition of a Tier 1 Advice Letter, the final CPUC Fire-Threat Map.

Additional information can be found here.

Source:

Ontario Land Cover (OLC) is a primary data layer. It provides a comprehensive, standardized, landscape level inventory of Ontario’s natural, rural and anthropogenic (human made) features.Product Packages:Esri-compatible PackageOpen source compatible PackageService:Now also available through a web service which circumvents the need to download data by exposing it for visualization over the internet. When using the ESRI Image Server URL in ESRI software full geoprocessing and analysis can also be done using just the service URL.Services can be accessed directly in ArcPro by using Add Data -> Add Data From Path and copying the desired service URL below into the text box. They can also be accessed by setting up an ArcGIS server connection in ESRI software using the ArcGIS Image Server REST endpoint URL.Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS Rest Server, WCS, WMS/WMTS) and add the appropriate URL in the resultant popup window.. All services are in Web Mercator projection.For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca.Service URL’sArcGIS Image Server Resthttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Baseline_V1/ImageServerWeb Mapping Service (WMS)https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/Thematic/Ontario_Land_Cover_Baseline_V1/ImageServer/WMSServer/Web Coverage Service (WCS)https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/Thematic/Ontario_Land_Cover_Baseline_V1/ImageServer/WCSServer/Additional DocumentationBaseline Class Descriptions - Ontario Land Cover Version 1 (TEXT)Changes Descriptions - Ontario Land Cover Version 1 (TEXT)StatusCompleted: Production of the data has been completedMaintenance and Update FrequencyAs needed: Data is updated as deemed necessaryContactJoel Mostoway, Natural Resources and Forestry, Science and Research Branch, joel.mostoway@ontario.ca