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:

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.

In the United States, the federal government manages approximately 28% of the land in the United States. Most federal lands are west of the Mississippi River, where almost half of the land by area is managed by the federal government. Federal lands include 193 million acres managed by the US Forest Service in 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 ManagementDepartment of DefenseNational Park ServiceUS Fish and Wildlife ServiceUS Forest ServiceDataset SummaryPhenomenon Mapped: United States federal lands managed by six federal agenciesGeographic Extent: 50 United States and the District of Columbia, 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.Data Coordinate System: WGS 1984Visible 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 3.0Publication Date: Various - Esri compiled and published this layer in May 2025. 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 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.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

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 …Show full descriptionThe 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: "Add Data" then choose GIS Servers from the "Look in" drop down. 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.

Coastline for Antarctica created from various mapping and remote sensing sources, consisting of the following coast types: ice coastline, rock coastline, grounding line, ice shelf and front, ice rumple, and rock against ice shelf. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. High resolution versions of ADD data are suitable for scales larger than 1:1,000,000. The largest suitable scale is changeable and dependent on the region.

Major changes in v7.5 include updates to ice shelf fronts in the following regions: Seal Nunataks and Scar Inlet region, the Ronne-Filchner Ice Shelf, between the Brunt Ice Shelf and Riiser-Larsen Peninsula, the Shackleton and Conger ice shelves, and Crosson, Thwaites and Pine Island. Small areas of grounding line and ice coastlines were also updated in some of these regions as needed.

Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.

Further information and useful links

Map projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap.

The currency of this dataset is May 2022 and will be reviewed every 6 months. This feature layer will always reflect the most recent version.

For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue.

A related medium resolution dataset is also published via Living Atlas, as well medium and high resolution polygon datasets.

For background information on the ADD project, please see the British Antarctic Survey ADD project page.

Lineage

Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each line has attributes detailing the source which can give the user further indications of its suitability for specific uses. Attributes also give information including 'surface' (e.g. grounding line, ice coastline, ice shelf front) and revision date. Compiled from sources ranging in time from 1990s-2022 - individual lines contain exact source dates.

Note: Find data at source. ・ 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.

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.

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.

Major libraries used: Osmar - To import data from open street maps Leaflet, mapview, sp - Mapping the network dplyr, plyr - To manipulate data frames Data used: The data used for creating the freeway network is obtained from open street maps, whereas, the data for sensors is obtained from PeMS. Methodology Creating the map required We start by downloading the bulk osm data for California (approx. 18GB). Next, using osmar library, we extract the required data using a bounding box, demarcating the latitude and longitude boundaries. bbox = corner_bbox(-118.0042, 33.6363, -117.7226, 33.9194) Then extracting only the freeway information (links and nodes) from the resulting data. The problem here is that most of the links contain more than two nodes, which would make the incidence matrix unnecessarily large. Thus, we extract the nodes that connect two links, and map them over the links. Creating the incidence matrix To create the incidence matrix, each link was looked up for the first and last node incident on it, keeping in mind the direction. The rows represent nodes, whereas the columns represent links. +1 was assigned at the head and -1 to the tail of the link, all other entries to 0. The incidence matrix has a dimension of 2973 by 2640. Assigning links to appropriate freeways The osm data does not explicitly mention about which freeway is a particular link part of. Thus, for the freeway links a combination of "name" and "ref" tag was used to obtain the information about the freeway. Adding ramps to the Fwy data frame So far, we only have freeway links assigned to the appropriate freeways. Now, we would like to add the immediate links that go off or on the freeway, aka ramps, to the data frame Fwy. This is done by checking for each node on the freeway segment, out of the links it is incident upon, which one is tagged as "motorway-link" in the osm data. If there is such a link, it was added to the Fwy data set with appropriate freeway values. Overlapping sensors over the network The sensor data is now used to add a layer over the existing graph to help visualize the complete network. Different types of sensors are grouped separately and can be viewed as per user's choice by clicking the check boxes. The legend shows the color used for each sensor type. Hovering upon the sensor, link or node highlights their IDs. By default, main line (ML) sensors are checked. Mapping sensors to the appropriate links Currently, by visualization we can figure out the link that contains a particular sensor. As PeMS sensor metadata does not interact with the osm data, the link-sensor relation is unknown. We need to create an algorithm such that each sensor automatically gets mapped to the link using the geographical properties. The algorithm for mapping sensors to the links is as follows: For each sensor location, extract all the links on the freeway segment in the direction sensor is installed For the nodes on each link, calculate 3 distances Distance between the nodes (d1) Distance between first node and sensor (d2) Distance between last node and sensor Calculate d1 - (d2+d3), call it d4 Calculate d4 for each link, and arrange d4 in ascending order The link for which d4 is smallest and lesser than a threshold (1e-4 in this case), assign it the sensor Repeat the above steps for each sensor location Finally, the results are stored as a form of a list (linkId). For illustration purpose, 5 ML sensors on I-5, highlighted on the map, are shown below with the appropriate link chosen by the algorithm. One can verify the IDs by hovering above the links in the map and cross checking with the table that appears below. Re-mapping ramp and freeway-freeway sensors Looking closely, one would figure out that the ramp sensors (OR/FR) are located on the freeways rather than ramps. Same for freeway-Freeway (FF) sensors. This was one of the tedious challenges I encountered in this project. But with a combination of a simple algorithm and manual work, the sensors were remapped. The details are omitted in this document. In the map below, all the remapped sensors are shown on their appropriate new links. Creating the adjacency matrix To create adjacency matrix, for each link, nodes having 1 or -1 were searched in the incidence matrix. The cell corresponding to these nodes in the adjacency matrix was assigned 1, else 0. Conclusion The results of this project, namely, incidence matrix, adjacency matrix and link-sensor relation data frame were used for the network sensor error estimation algorithm. This project led to the application of the error estimation algorithm on large networks, which is expected to result in an important contribution to the field of sensor bias estimation. The aim of this script is to automate the process of directed network graph formation, i.e., creation of incidence matrix, node adjacency matrix, and map the sensors to appropriate links. The data used fo...

This map contains a compilation of Utah WRI project proposals to promote interagency collaboration. To add your project, submit a record using survey123: https://arcg.is/1yi98j1

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.GAP 1 and 2 areas are primarily managed for biodiversity, GAP 3 are managed for multiple uses including conservation and extraction, GAP 4 no known mandate for biodiversity protection. Provides a general overview of protection status including management designations. PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.The USGS Protected Areas Database of the United States (PAD-US) classifies lands into four GAP Status classes:GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protectionIn the United States, areas that are protected from development and managed for biodiversity conservation include Wilderness Areas, National Parks, National Wildlife Refuges, and Wild & Scenic Rivers. Understanding the geographic distribution of these protected areas and their level of protection is an important part of landscape-scale planning. Dataset SummaryPhenomenon Mapped: Areas protected from development and managed to maintain biodiversity Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: USGS Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, or 3GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

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.

The European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5) of the European Space Agency.

The WorldCover 2021 v200 product is developed by a consortium lead by VITO Remote Sensing together with partners Brockmann Consult, Gamma Remote Sensing AG, IIASA and Wageningen University.

Data description

The ESA WorldCover 10m 2021 V200 is provided per 3 x 3 degree tile, 2651 in total. Each tile contains a set of 2 Cloud Optimized GeoTIFF (COG) files corresponding to the following data layers:

• Map: Land cover map with 11 classes

• InputQuality: Three band GeoTIFF providing three per pixel quality indicators of the Sentinel1 and Sentinel-2 input data

Tiles are provided in EPSG:4326, geographic projection (latitude/longitude CRS).

For more information on the ESA WorldCover product and details on how to use the data please see the Product User Manual for WorldCover 2021 v200.

Data publication: 2022-10-28

Citation:

To cite these maps as data source in your publication, please add:

WorldCover 2021 v200

Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P., Ramoino, F., Arino, O., 2022. ESA WorldCover 10 m 2021 v200 (https://doi.org/10.5281/zenodo.7254221).

Contact points:

Resource Contact: ESA/VITO/Brockmann Consult/CS/GAMMA Remote Sensing/IIASA/WUR

Resource Contact: European Space Agency

Metadata Contact: FAO-Data

Data lineage:

The ESA WorldCover 10m 2021 v200 product updates the existing ESA WorldCover 10m 2020 v100 product to 2021 but is produced using an improved algorithm version (v200) compared to the 2020 map. Consequently, since the WorldCover maps for 2020 and 2021 were generated with different algorithm versions (v100 and v200, respectively), changes between the maps should be treated with caution, as they include both real changes in land cover and changes due to the algorithms used.

The ESA WorldCover product has been independently validated by Wageningen University (statistical accuracy) and IIASA (spatial accuracy). The WorldCover 2021 v200 reaches an overall accuracy of 76.7%. For more details please see the Product Validation Report V2.0.

Resource constraints:

The ESA WorldCover product is provided free of charge, without restriction of use. If you are using the data as a layer in a published map, please include the following attribution text:

Publications, models and data products that make use of these datasets must include proper acknowledgement, including citing the datasets and the journal article as in the following citation.

© ESA WorldCover project 2021 / Contains modified Copernicus Sentinel data (2021) processed by ESA WorldCover consortium.

Online resources:

Data access

WorldCover Product User Manual V.2.0

WorldCover Product Validation Report V2.0

Worldwide land cover mapping website

This map is created and published by Stamen Design. Reminiscent of hand drawn maps, our watercolor maps apply raster effect area washes and organic edges over a paper texture to add warm pop to any map. Watercolor was inspired by the Bicycle Portraits project. Thanks to Cassidy Curtis for his early advice.

This dataset contains model-based county-level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2022 county population estimates, and American Community Survey (ACS) 2018–2022 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the census 2022 county boundary file in a GIS system to produce maps for 40 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

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.

Context of the project Knowledge of the level of rents is important to ensure the proper functioning of the rental market and the conduct of national and local housing policies. The Directorate-General for Planning, Housing and Nature (DGALN) launched in 2018 the “rent map” project by partnering on the one hand with a research team in economics of Agrosup Dijon and the National Institute of Research in Agronomics (INRAE), and on the other hand with SeLoger and leboncoin. In 2020, the project was taken over by the National Agency for Housing Information (ANIL), which published a new version of the map in 2022. This innovative partnership has rebuilt a database with more than 7 million rental ads. On the basis of these data, the research team and ANIL have developed a methodology for estimating indicators, at the communal scale, of rent (including charges) per m² for apartments and houses. These experimental indicators are put online in order to be usable by all: state services, local authorities, real estate professionals, private donors and tenants. From 2022, the maps are updated and published annually by ANIL. This project provides additional information to that offered by the Local Land Observatorys (OLL), deployed since 2013 and reinforced since 2018 by the Elan law. Today, this associative network of around thirty OLs publishes precise information every year on rents in some 50 French agglomerations. Presentation of the dataset The data disseminated are indicators of ad rents, at the level of the municipality. The field covered is the whole of France, outside of Mayotte. The geography of the municipalities is that in force on 1 January 2022. Rent indicators are calculated through the use of ad data published on the platforms of leboncoin and Groupe SeLoger over the period -2018-2022. Rent indicators are provided including charges for empty leased standard properties and leased in Q3 2022 with the following reference characteristics: — For an apartment (all types combined): 52 m² and average area per room of 22.2 m² — For apartment type T1-T2: surface area of 37 m² and average area per room of 22.9 m² — For apartment type T3 or more: area of 72 m² and average area per room of 21.2 m² — For a house: area of 92 m² and average area per room of 22.3 m² Conditions for data use These indicators can be freely used, provided that the source is indicated as follows: ANIL estimates, based on data from the SeLoger Group and leboncoin. Precautions of employment Rent indicators are calculated on unfurnished property and expenses included, on ad data. The data were duplicated but could not rely on very discriminating photos and characteristics. The method of meshing implies, for municipalities with no dwellings rented via an advertisement on at least one of the two sites during the period considered, which rent indicator is that estimated for a larger mesh comprising neighbouring municipalities with similar characteristics. Users are advised to consider rent indicators with caution in municipalities where the coefficient of determination (R2) is less than 0.5, the number of observations in the municipality is less than 30 or the prediction interval is very wide. **In addition, compared to the previous version of the indicators published in 2020, this new map does not allow to measure changes in rent, due to differences in the communal mesh size and changes in methodology. **

This stocktaking activity aims at collecting metadata information on the georeferenced soil data available in the EJP-SOIL countries. This stocktaking concerns not just the soil data itself, but also auxiliary information needed for the soil mapping activity, and the mapping experience hold in our institutions. Available doesnt mean that this information is freely available, but just that it exists, with a specific data owner (which can also be different from your institution) and a specific sharing policy. The first sheet, named description of data sources, is to insert the list of your data sources. We have put some Italian examples to help you understanding the kind of information to be inserted in this sheet (you can delete them). Basically, it is a list of data sources available, either for basic soil data (point data or mapped data), and for auxiliary data. Among auxiliary data we are also looking for mapped data on soil management. Once the first sheet is compiled, the listed data sources will constitute a drop-down list to be used in the compilation of the following sheets. The second sheet, named soil property_data (SP), is for the compilation of the soil property data available in your basic soil data sources. It is most probable that more the one data source exists in your country, storing soil data properties. Each one of these soil data sources should have been described in the first sheet. Then, the soil properties store in each soil data source should be inserted in the second sheet. For each soil property it is requested to indicate the unit of measure used and the analytical method(s) used (can be more then one). In order to help you in the compilation, we have listed, in the third "methods" sheet, the most commonly used analytical methods, but you can add more methods if you adopt different ones. If the data source list is a soil map already published, we are asking you to compile the method used for mapping. In the fourth sheet, named soil management (MG), you can list the kind of soil management practices which are available in you data sources. We must stress here, that the data sources for soil management that we are looking for, are georeferenced data sources. The last 2 sheets are the drop-down lists used in the questionnaire and a description of the terms used.

This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

The initiative started from the Aurora Universities Network in 2017, in the working group "Societal Impact and Relevance of Research", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).

Context of this dataset | classification model improvement workflow

The classification model we have used are 17 different search queries on the Scopus database.

• SDG search queries version 4.0 (SQv4) have been created, Published here:
  • Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443
• A survey has been distributed to senior researchers to test the robustness of SQv4. Published here:
  • Survey data of "Mapping Research output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385
• This text analysis has been made as one of the inputs to improve the classification model. Published here:
  • Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090
• Improved SDG search queries version 5.0 (SQv5) have been created, Published here:
  • Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445

Methods used to do the text analysis

1. Term Extraction: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword
2. Contrast analysis: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.
3. Network map: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).
4. Topic model: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.
5. Contingency matrix: This diagram shows the top 10 of co-occurring terms that correlate the most.

Software used to do the text analyses

CorTexT: The CorTexT Platform is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.

Resource with interactive visualisations

Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. https://sites.google.com/vu.nl/sdg-survey-analysis-results/

Data set content

In the dataset root you'll find the following folders and files:

• /sdg01-17/
  • This contains the text analysis for all the individual SDG surveys.
• /methods/
  • This contains the step-by-step explanations of the text analysis methods using Cortext.
• /images/
  • images of the results used in this README.md.
• LICENSE.md
  • terms and conditions for reusing this data.
• README.md
  • description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

Inside an /sdg01-17/-folder you'll find the following:

• This contains the step-by-step explanations of the text analysis methods using Cortext.
• /sdg01-17/sdg04-sdg-survey-selected-publications-combined.db
  • his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents
• /sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db
  • same as above, but only the accepted papers
• /sdg01-17/extracted-terms-list-top1000.csv
  • the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.
• /sdg01-17/contrast-analysis/
  • This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.
• /sdg01-17/topic-modelling/
  • This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.
• /sdg01-17/network-mapping/
  • This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers
• /sdg01-17/contingency-matrix/
  • This contains the data and visualisation of the top 10 terms co-occuring

note: the .csv files are actually tab-separated.

Contribute and improve the SDG Search Queries

We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. https://github.com/Aurora-Network-Global/sdg-queries