An ArcGIS Pro project may contain maps, scenes, layouts, data, tools, and other items. It may contain connections to folders, databases, and servers. Content can be added from online portals such as your ArcGIS organization or the ArcGIS Living Atlas of the World.In this tutorial, you'll create a new, blank ArcGIS Pro project. You'll add a map to the project and convert the map to a 3D scene.Estimated time: 10 minutesSoftware requirements: ArcGIS Pro

To create this app:Make a map of the AfDB projects CSV file in the Training Materials group.Download the CSV file, click Map (at the top of the page), and drag and drop the file onto your mapFrom the layer menu on your Projects layer choose Change Symbols and show the projects using Unique Symbols and the Status of field.Make a second map of the AfDB projects shown using Unique Symbols and the Sector field.HINT: Create a copy of your first map using Save As... and modify the copy.Assemble your story map on the Esri Story Maps websiteGo to storymaps.arcgis.comAt the top of the site, click AppsFind the Story Map Tabbed app and click Build a Tabbed Story MapFollow the instructions in the app builder. Add the maps you made in previous steps and copy the text from this sample app to your app. Explore and experiment with the app configuration settings.=============OPTIONAL - Make a third map of the AFDB projects summarized by country and add it to your story map.Add the World Countries layer to your map (Add > Search for Layers)From the layer menu on your Projects layer choose Perform Analysis > Summarize Data > Aggregate Points and run the tool to summarize the projects in each country.HINT: UNCHECK "Keep areas with no points"Experiment with changing the symbols and settings on your new layer and remove other unnecessary layers.Save AS... a new map.At the top of the site, click My Content.Find your story map application item, open its Details page, and click Configure App.Use the builder to add your third map and a description to the app and save it.

Interactive GIS Mapping Tool – Urgent Drinking Water Needs (UDWN) Web Map in California

Use Constraints:

This mapping tool is for reference and guidance purposes only and is not a binding legal document to be used for legal determinations. The data provided may contain errors, inconsistencies, or may not in all cases appropriately represent the current status of Urgent Drinking Water Needs project locations. The data in this map are subject to change at any time and should not be used as the sole source for decision making. By using this data, the user acknowledges all limitations of the data and agrees to accept all errors stemming from its use. The Urgent Drinking Water Needs map does not provide the locations of individual households that were provided funding through grant agreements with non-profit organizations.

Description:

This map displays Urgent Drinking Water Needs due to drought, contamination, or other eligible emergencies. This includes projects approved for funding from July 1, 2014 to November 18, 2022, including both active and completed projects. The data comes from the State Water Resources Control Board (SWRCB) Cleanup and Abatement Account’s (CAA) project database and was exported on November 18, 2022. The map contains four layers: UDWN_Projects, UDWN_Summary_by_county, CA_Assembly_Districts_WEB, and CA_Senate_Districts_WEB.

The attributes for each project in the UDWN_Projects layer include the recipient of grant funding (grantee), community served, type of project, grant amount, funding program, date the project was approved, date the project was completed, Disadvantaged Community status, Small Disadvantaged Community status, the public water system number, status of the project (Active or Completed), and the state fiscal year in which the project was approved.

How to Use the Interactive Mapping Tool:When the map loads, it displays the state of California, UDWN Project locations, and California county boundaries. The “About” tab is located on the left-hand side of the map and displays instructions for using the map. The next tab display pre-set filters, the legend, and a layer list. Clicking on the “Legend” tab in the menu will show the legend of the map. Projects that appear as blue dots are still active, while projects that appear as red dots have already been completed.Note: Layers that show CA Assembly and Senate Districts were created by the Sierra Nevada Conservancy (SNC). These layers must be toggled on in the layers list to be seen. To view information about a specific project, click on a project location. A pop-up box will appear with the following information: (a) county name, (b) community served, (c) type of project, (d) approved funding amount, (e) approval date, and (f) status. To view information about the total funding and number of projects in a county, click within a county boundary and a pop up will appear.Use the pre-set filters to filter projects by status, fiscal year, funding program, county, assembly district, and/or senate district using the drop-down menu. The filters can be toggled on or off using the switches on the right side of the menu. To create a custom filter, click the filter icon at the bottom of the preset filter menu and enter the desired parameters. For one parameter, click “add expression” to create a custom filter. For more than one, click “add set” to create a custom filter.To export and download filtered data, open the Attribute Table located at the bottom of the map, click the “Options” drop down menu, select “Export all to CSV” from the drop-down menu, and download the desired information.

Map Layers:UDWN_Projects – This layer shows all active or completed UDWN projects from July 1, 2014 to November 18, 2022. Active projects are represented with blue dots while completed projects are represented with red dots. The attributes in this layer include what county the project is in, the community served, the type of project, approved funding amount, approval date, and status.UDWN_Summary_by_county – This layer shows the boundary lines for all the counties in California. The attributes in this layer include the total number of projects and total funding approved in that county since July 1, 2014. CA_Assembly_Districts_WEB – This layer shows the boundary lines for all the assembly districts in California. It is owned and maintained by the Sierra Nevada Conservancy (SNC) and boundaries may not be accurate. CA_Senate_Districts_WEB – This layer shows the boundary lines for all the senate districts in California. It is owned and maintained by the Sierra Nevada Conservancy (SNC) and boundaries may not be accurate.

Informational Pop-up Box:County – California county where the project is locatedCommunity Served – California community that is benefiting from UDWN funding Type of Project – Project type, which can include bottled water, consolidation, hauled water, pilot study, POU, pump, tank, treatment, and well Approved Funding Amount – Amount of money in U.S. dollars approved for the projectApproval Date – Date that the project was approved for fundingStatus – Current status of the project (active or closed)Date Created:

Data created on November 18, 2022 and valid up to this date.

Sources:

Urgent Drinking Water Needs data was exported from the CAA Database.

The Sierra Nevada Conservancy (SNC) created the California Senate and Assembly layers.

Points of Contact:

Christina Raynard is the creator and owner of this layer. Christina.raynard@waterboards.ca.gov (State Water Resources Control Board, Division of Financial Assistance)

Terms of Use

No special restrictions or limitations on using the item’s content have been provided.

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Instrumental to the photo interpretive effort was the use of the GPS located vegetation plots collected by the field crew. These plots provided an idea of what the signatures of the individual map units should look like. In addition to the tablular data associated with each vegetation plot were five photographs collected at each plot. These photographs helped not only in identifying the immediate area but also provided us with a “look” at the areas surrounding the vegetation plot which might be a different map unit. These photographs may be “hyperlinked” within ArcMap to the salient vegetation observation point for a better concept of on the ground conditions.All interpreted mylar layers were scanned at 300 dpi. Each scanned mylar was then rectified to the NAIP base layer using recognizable ground features as registration points. The resulting scan produced a raster image that was subsequently vectorized. Each vectorized output was then extensively edited to produce clean digital vector lines. From the digitized vectors we created polygons by building topology in the GIS program. Finally, we created labels for each polygon and used these to add the attribute information. Attribution for all the polygons at CHIC included information pertaining to map units, NVC associations, Anderson land-use classes, and other relevant data. Attribute data were taken directly from the interpreted photos or were added later using the orthophotos as a guide.

The eAtlas delivers its mapping products via two Web Mapping Services, a legacy server (from 2008-2011) and a newer primary server (2011+) to which all new content it added. This record describes the legacy WMS.

This service delivers map layers associated with the eAtlas project (http://eatlas.org.au), which contains map layers of environmental research focusing on the Great Barrier Reef. The majority of the layers corresponding to Glenn De'ath's interpolated maps of the GBR developed under the MTSRF program (2008-2010).

This web map service is predominantly maintained for the legacy eAtlas map viewer (http://maps.eatlas.org.au/geoserver/www/map.html). All the these legacy map layers are available through the new eAtlas mapping portal (http://maps.eatlas.org.au), however the legends have not been ported across.

This WMS is implemented using GeoServer version 1.7 software hosted on a server at the Australian Institute of Marine Science.

For ArcMap use the following steps to add this service: 1. "Add Data" then choose GIS Servers from the "Look in" drop down. 2. Click "Add WMS Server" then set the URL to "http://maps.eatlas.org.au/geoserver/wms?"

Note: this service has around 460 layers of which approximately half the layers correspond to Standard Error maps, which are WRONG (please ignore all *Std_Error layers.

This services is operated by the Australian Institute of Marine Science and co-funded by the MTSRF program.

The eAtlas delivers its mapping products via two Web Mapping Services, a legacy server (from 2008-2011) and a newer primary server (2011+) to which all new content it added. This record describes the primary WMS.

This service delivers map layers associated with the eAtlas project (http://eatlas.org.au), which contains map layers of environmental research focusing on the Great Barrier Reef and its neighbouring coast, the Wet Tropics rainforests and Torres Strait. It also includes lots of reference datasets that provide context for the research data. These reference datasets are sourced mostly from state and federal agencies. In addition to this a number of reference basemaps and associated layers are developed as part of the eAtlas and these are made available through this service.

This services also delivers map layers associated with the Torres Strait eAtlas.

This web map service is predominantly set up and maintained for delivery of visualisations through the eAtlas mapping portal (http://maps.eatlas.org.au) and the Australian Ocean Data Network (AODN) portal (http://portal.aodn.org.au). Other portals are free to use this service with attribution, provided you inform us with an email so we can let you know of any changes to the service.

This WMS is implemented using GeoServer version 2.3 software hosted on a server at the Australian Institute of Marine Science. Associated with each WMS layer is a corresponding cached tiled service which is much faster then the WMS. Please use the cached version when possible.

The layers that are available can be discovered by inspecting the GetCapabilities document generated by the GeoServer. This XML document lists all the layers, their descriptions and available rendering styles. Most WMS clients should be able to read this document allowing easy access to all the layers from this service.

For ArcMap use the following steps to add this service: 1. "Add Data" then choose GIS Servers from the "Look in" drop down. 2. Click "Add WMS Server" then set the URL to "http://maps.eatlas.org.au/maps/wms?"

Note: this service has over 1000 layers and so retrieving the capabilities documents can take a while.

This services is operated by the Australian Institute of Marine Science and co-funded by the National Environmental Research Program Tropical Ecosystems hub.

Pepco supports decarbonization and electrification impacts by providing timely and cost effective connections to the distribution grid for new customer loads and distributed generation.

Pepco anticipates rapid adoption of new electric loads that support decarbonization such as electric vehicle charging infrastructure and converting to electric heat sources. In order to help guide large scale electrification Pepco has developed a load capacity map to represent areas on the distribution grid where there is reasonable capacity to accommodate electric vehicle charging infrastructure and other load sources with lower probability of necessitating extensive equipment upgrades or line extensions that would add cost or time to projects. The map provides different levels of available load capacity on a circuit by color (Green greater than 1 MW, Yellow 0.5 MW to 1 MW, and Red less than 0.5 MW). The map also provides areas that only have single or two phase service available in magenta and would likely require system upgrades for load demands greater than 100kW. For project requiring greater than 4 MW of load demand a new express feeder would be required to service the load. The map linked below shows general areas where load capacity may be coming constrained and could require system upgrade scope to accommodate new load project connections. Click the button below to access a searchable version; type an address into the search box to locate a specific location.

Retirement Notice: This item is in mature support as of June 2026 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Use this country model layer when performing analysis within a single country. This layer displays a single global land cover map that is modeled by country for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create these predictions.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: GlobalCell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.”This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.What these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegionsSlopeTemperature Qualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture Were small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican City Index to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover.1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

In the United States, the federal government manages lands in significant parts of the country. These lands include 193 million acres managed by the US Forest Service in the nation's 154 National Forests and 20 National Grasslands, Bureau of Land Management lands that cover 247 million acres in Alaska and the Western United States, 150 million acres managed for wildlife conservation by the US Fish and Wildlife Service, 84 million acres of National Parks and other lands managed by the National Park Service and over 30 million acres managed by the Department of Defense. The Bureau of Reclamation manages a much smaller land base than the other agencies included in this layer but plays a critical role in managing the country's water resources.The agencies included in this layer are:Bureau of Land ManagementBureau of ReclamationDepartment of DefenseNational Park ServiceUS Fish and Wildlife ServiceUS Forest ServiceDataset SummaryPhenomenon Mapped: United States lands managed by six federal agencies Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, US Virgin Islands, Guam, American Samoa, and Northern Mariana Islands. The layer also includes National Monuments and Wildlife Refuges in the Pacific Ocean, Atlantic Ocean, and the Caribbean Sea.Visible Scale: The data is visible at all scales but draws best at scales greater than 1:2,000,000Source: BLM, DoD, USFS, USFWS, NPS, PADUS 2.1Publication Date: Various - Esri compiled and published this layer in May 2022. See individual agency views for data vintage.There are six layer views available that were created from this service. Each layer uses a filter to extract an individual agency from the service. For more information about the layer views or how to use them in your own project, follow these links:USA Bureau of Land Management LandsUSA Bureau of Reclamation LandsUSA Department of Defense LandsUSA National Park Service LandsUSA Fish and Wildlife Service LandsUSA Forest Service LandsWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "federal lands" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "federal lands" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in Pro.The data can be exported to a file geodatabase, a shapefile or other format and downloaded using the Export Data button on the top right of this webpage.This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

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

Project Page

https://open-earth-map.org/

Paper

https://arxiv.org/abs/2210.10732

Overview

OpenEarthMap is a benchmark dataset for global high-resolution land cover mapping. OpenEarthMap consists of 5000 aerial and satellite images with manually annotated 8-class land cover labels and 2.2 million segments at a 0.25-0.5m ground sampling distance, covering 97 regions from 44 countries across 6 continents. OpenEarthMap fosters research including but not limited to semantic segmentation and domain adaptation. Land cover mapping models trained on OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications.

Reference

@inproceedings{xia_2023_openearthmap, title = {OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping}, author = {Junshi Xia and Naoto Yokoya and Bruno Adriano and Clifford Broni-Bediako}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {6254-6264} }

License

Label data of OpenEarthMap are provided under the same license as the original RGB images, which varies with each source dataset. For more details, please see the attribution of source data here. Label data for regions where the original RGB images are in the public domain or where the license is not explicitly stated are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Note for xBD data

The RGB images of xBD dataset are not included in the OpenEarthMap dataset. Please download the xBD RGB images from https://xview2.org/dataset and add them to the corresponding folders. The "xbd_files.csv" contains information about how to prepare the xBD RGB images and add them to the corresponding folders.

Code

Sample code to add the xBD RGB images to the distributed OpenEarthMap dataset and to train baseline models is available here.

Leaderboard

Performance on the test set can be evaluated on the Codalab webpage.

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale. When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or do not own a car? How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards. The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access. Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples). The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer. Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters. The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis. The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels. The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer. Methodology Every census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway. A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in. The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle). The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step. Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect. Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point. Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The NOAA Hypoxia Watch project provides near-real-time, web-based maps of dissolved oxygen near the sea floor over the Texas-Louisiana continental shelf during a period that extends from mid-June to mid-July. The NOAA National Marine Fisheries Service Mississippi Laboratories at Pascagoula and Stennis Space Center and the NOAA's National Centers for Environmental Information (NCEI) began the Hypoxia Watch project in 2001. Scientists aboard the NOAA Research Vessel Oregon II measure seawater properties, such as water temperature, salinity, chlorophyll, and dissolved oxygen, as the Oregon II cruises the waters south of Pascagoula, MS and then makes its way from Brownsville, Texas, to the mouth of the Mississippi River. A scientist aboard the ship processes the measurements from electronic dissolved oxygen sensors, checks the measurements periodically with chemical analyses of the seawater, then sends the data by FTP to the NCEI approximately every three to four days. Physical Scientists at NCEI transform the dissolved oxygen measurements into contour maps, which identify areas of low oxygen, or hypoxia. During the cruise, as the data is received from the ship, NCEI generates new maps and publishes them on the web. The first map will usually cover an area off the Mississippi coast, successive maps will add areas of the continental shelf from Brownsville to Corpus Christi, and the final map will usually cover the entire Texas-Louisiana-Mississippi coast. Maps are published every three to four days from approximately June 22 to July 20.

This project will support two existing MAP projects: 1) Tree Islands in Everglades National Park (ENP) - Big Cypress and 2) Tree Island Stage Duration and the measurement of water depth on tree islands located in WCA 3A and 3B. While these projects have similar broad objectives, some of the specific monitoring design and constituents differ. Tree island elevations and species composition were measured on over 200 islands in WCA 3, while community dynamics, hydrology, soil moisture, and transpiration and growth rates of the dominant trees are monitored on tree islands located within ENP. The ENP monitoring program was based on work originally funded by the ENP in 2005. Ross and Oberbauer (2006) have shown large seasonal differences between dry and wet season transpiration rates in an island located in the eastern prairies of ENP. However, no seasonal differences were observed in the islands located in Shark Slough where the dry season water levels remained above the marsh surface. These results have implications for the management of these systems, since it appears that extended dry-downs during the November – May dry season may cause significant declines in tree island productivity. These changes in productivity, in turn, may alter the role tree islands play in nutrient cycles in the Everglades marshes. The role of tree islands in marsh nutrient cycles has been the subject of recent journal articles (Ross et al. 2006, Wetzel et al. 2005), and in the last year RECOVER has sponsored several discussions dedicated to the development of tree island conceptual models and performance measures (e.g. GEER 2008). The WCA 3 monitoring effort has begun to add many of the physiological measurements to their monitoring program, but is currently lacking the funding necessary to fully implement the effort. This work will place all the existing MAP projects within a common framework that links tree island productivity with nutrient cycles and hydrologic conditions in the marsh. The results are expected to provide information useful for the development of cost-effective, long-term monitoring tools for the MAP. The overall objective of the study is to test the transpiration model presented by Ross et al. (2006 and Wetzel et al. (2005), which states that high transpiration is the driving force for nutrient accumulation of tree islands (Fig 1). According to this model, slough tree islands can maintain high transpiration rates during the dry season using standing marsh water around the island or groundwater, while prairie tree islands will have low transpiration rate during dry season due to low water availability. Thus, the difference in hydroperiod between slough and prairie tree islands will result in differential nutrient accumulation rates and suggests that slough tree islands can accumulate more nutrients than prairie tree islands.

The specific objectives of this study are 1) to test whether there is a decrease in transpiration from wet to dry season in prairie tree islands but not in slough tree islands, 2) to test whether the above transpiration shift is reflected in the foliar carbon isotope ratios and 3) to test whether prairie tree islands showing the decrease from wet to dry season transpiration have lower foliar nutrient concentrations compared to slough tree islands. The objective is to use Granier probes to measure sap flux rates as a proxy for transpiration, foliar carbon and nitrogen isotope analysis as well foliar phosphorus and nitrogen concentrations to answer the above questions. The results of this project will be submitted to a peer reviewed journal. The results of this research will link transpiration with nutrient accumulation and will provide guidelines to tree island models. In addition, the foliar isotopic analysis, if consistent with the above hypotheses, will provide a measure of tree island nutrient stability and may be suitable for use as a MAP monitoring tool.

This dataset contains a set of data related to processed information deriving from fieldwork activities and elaboration of archival information and literature (v. Cat 3, Cat 4, Cat 6 Deliverable D6.2, DMP) collected, accessed, and consulted during the WP1 and WP2 research activities. It constitutes a fundamental part that supports the Land-In-Pro spatial and territorial analysis in WP3, which encompasses activities aimed at informing a webGIS that visualises the transformations the selected context/site has undergone over the years in the pilot site, capturing its former and current conditions and configurations, whilst allowing the definition of indicators for the development of the Land-In-Pro Assessment Tool. The Land-In-Pro Pilot Site Mapping GIS project has been structured to ensure usability for both expert GIS users and non-specialist audiences. The project has been set up by using the open-source mobile application Qfield (v.3.4) during fieldwork, and QGIS (v.3.34 Prizren) during the data processing phase. Set in the Roma40 reference system and Gauss–Boaga cartographic projection (EPSG:3003), it is organised into three main layers (zoning, buildings_mapping, views_mapping) with attribute tables containing information available in both Italian and English languages. The buildings_demolished layer is a support vector layer used only for spatial reference: it provides indicative geometries to georeference demolished buildings. For optimal use of the project, it is recommended to add a base map (e.g., Google Satellite or Bing Maps Satellite Imagery) within QGIS. This provides a clear cartographic background that facilitates the orientation and interpretation of the mapped data. This dataset has been curated by Dr Federica Pompejano and Dr Sara Mauri. It relates to:

• WP2
• Version 1.0; created on October 2, 2023; published on September 30, 2025.
• Federica Pompejano, Principal Investigator, RTD-A, Department Architecture and Design (DAD), Università di Genova (UniGe), federica.pompejano@unige.it (creator and depositor)
• Sara Mauri, Postdoctoral researcher, Department Architecture and Design (DAD), Università di Genova (UniGe), sara.mauri@edu.unige.it (depositor)
• Data originates from the collection of spatial data during fieldwork activities and from the processing of archival sources and literature.
• Keywords: NextGenerationEU, NextGenEu, Ministero dell'Università e della Ricerca, MUR, PNRR, UniGe, DAD, MSCA_0000005, Land-In-Pro, Post-industrial landscape, Industrial heritage, Landscape studies, Architectural conservation, Heritage studies, Fieldwork, Photo survey, Data processing, Architetture, Strutture, Paesaggio. Architecture, Landscape, Structures
• Link: https://landinpro.unige.it/pilotsitemapping

This dataset is part of the Land-In-Pro project, which has received funding from the Ministry of University and Research, General Directorate for Internationalisation and Communication – National Recovery and Resilience Plan (PNRR) - Mission 4 “Education and Research” - Component 2 “From Research to Business” - Investment 1.2 “Funding projects presented by young researchers” and the European Union – Next Generation EU. The content of this database reflects only the authors’ views. The authors, Host Institution, Ministry of University and Research and the European Commission are not responsible for any use that may be made of the information it contains.

This dataset contains a QGIS project (.qgz) along with supporting files including PDFs (.pdf), images (.jpg), text (.txt), XML metadata (.xml), and QGIS packages (.qpkg).

The metadata are contained in a markdown README file (.txt). Metadata is compiled using the online tool DataCite Metadata Generator - Kernel 4.4 provided by DataCite Metadata Working Group. (2021). DataCite Metadata Schema Documentation for the Publication and Citation of Research Data and Other Research Outputs. Version 4.4. DataCite e.V. https://doi.org/10.14454/3w3z-sa82.

Land-In-Pro Pilot Site Mapping © 2025 by Land-In-Pro Project - Federica Pompejano and Sara Mauri, Department of Architecture and Design (DAD), Università di Genova (UniGe) is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). If not otherwise indicated, images were acquired by researchers during fieldwork and mapping activities conducted under Land-In-Pro research project's WP2 and WP3 within the territorial context of the pilot site (Ferrania, Cairo Montenotte, Savona, Italy). The information contained in each form is the result of a combined processing of raw fieldwork data and the elaboration of heterogeneous historical sources, including: archival materials (currently under inventory process) from the Ferrania Film Museum in Cairo Montenotte (SV); municipal building records (Municipal Archive of Building Practices, Municipality of Cairo Montenotte, Savona); and historical cadastral maps (Cadastral Map Collection, State Archives of Savona). All consultation permits were previously acquired. Consulted archive materials are available for on-site consultation under each archive's rules and conditions.

Wildfire - Fire Risk and Fire Responsibility Areas (CAL FIRE) for development of the Parcel Inventory dataset for the Housing Element Site Selection (HESS) Pre-Screening Tool.

** This data set represents Moderate, High, and Very High Fire Hazard Severity Zones in State Responsibility Areas (SRA) and Very High Fire Hazard Severity Zones in Local Responsibility Areas (LRA) for the San Francisco Bay Region and some of its surrounding counties. The data was assembled by the Metropolitan Transportation Commission from multiple shapefiles provided by the California Department of Forestry and Fire Protection. The SRA data was extracted from a statewide shapefile and the LRA data is a combination of county shapefiles. All source data was downloaded from the Office of the State Fire Marshal's Fire Hazard Severity Zones Maps page (https://osfm.fire.ca.gov/divisions/community-wildfire-preparedness-and-mitigation/wildland-hazards-building-codes/fire-hazard-severity-zones-maps/). **

State Responsibility Areas PRC 4201 - 4204 and Govt. Code 51175-89 direct CAL FIRE to map areas of significant fire hazards based on fuels, terrain, weather, and other relevant factors. These zones, referred to as Fire Hazard Severity Zones (FHSZ), define the application of various mitigation strategies to reduce risk associated with wildland fires.

CAL FIRE is remapping FHSZ for SRA and Very High Fire Hazard Severity Zones (VHFHSZ) recommendations in LRA to provide updated map zones, based on new data, science, and technology.

Local Responsibility Areas Government Code 51175-89 directs the CAL FIRE to identify areas of very high fire hazard severity zones within LRA. Mapping of the areas, referred to as VHFHSZ, is based on data and models of, potential fuels over a 30-50 year time horizon and their associated expected fire behavior, and expected burn probabilities to quantify the likelihood and nature of vegetation fire exposure (including firebrands) to buildings. Details on the project and specific modeling methodology can be found at https://frap.cdf.ca.gov/projects/hazard/methods.html. Local Responsibility Area VHFHSZ maps were initially developed in the mid-1990s and are now being updated based on improved science, mapping techniques, and data.

Local government had 120 days to designate, by ordinance, very high fire hazard severity zones within their jurisdiction after receiving the CAL FIRE recommendations. Local governments were able to add additional VHFHSZs. There was no requirement for local government to report their final action to CAL FIRE when the recommended zones are adopted. Consequently, users are directed to the appropriate local entity (county, city, fire department, or Fire Protection District) to determine the status of the local fire hazard severity zone ordinance.

In late 2005, to be effective in 2008, the California Building Commission adopted California Building Code Chapter 7A requiring new buildings in VHFHSZs to use ignition resistant construction methods and materials. These new codes include provisions to improve the ignition resistance of buildings, especially from firebrands. The updated very high fire hazard severity zones will be used by building officials for new building permits in LRA. The updated zones will also be used to identify property whose owners must comply with natural hazards disclosure requirements at time of property sale and 100 foot defensible space clearance. It is likely that the fire hazard severity zones will be used for updates to the safety element of general plans.

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

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.ca Available 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/ImageServer https://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) 2022 Central Ontario Orthophotography Project (COOP) 2021 South-Western Ontario Orthophotography Project (SWOOP) 2020 Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020 South Central Ontario Orthophotography Project (SCOOP) 2018 North-Western Ontario Orthophotography Project (NWOOP) 2017 Central Ontario Orthophotography Project (COOP) 2016 South-Western Ontario Orthophotography Project (SWOOP) 2015 Algonquin 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 year Contact:Geospatial Ontario (GEO), geospatial@ontario.ca