Important Note: This item is in mature support as of February 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. This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation,
clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

Last Update: 06/18/2025 with v10 launch and Reverse Geocode HotfixRequires ArcGIS Pro 3.3.xThis is a file structure with ArcGIS Pro project and layout templates for supporting Urban Search and Rescue Teams in 2024. It points to the latest feature layers and is based on the NWCG Wildfire GIS templates.Updates to this project can be found in the Read Me text document in the root folder of the template after downloading. Some patch notes can also be found below in the comments.Special thanks to NIFC and the Wildfire GIS Community for the starting template. For more documentation see NWCG Standards for Geospatial Operations, PMS 936 | NWCGYOU WILL NOT BE ABLE TO ACCESS any incident data unless you are a member of the NSARGC Group.If the template brings you to a screen saying "Invalid Token", you may need to try downloading it again. How to deploy templateThis template is not a traditional ArcGIS Pro template. When you download this template, you are downloading the full folder structure, pre-made map projects, layouts, databases, and tools that have been designed to work alongside SARCOP. This "template" does not use the "Create a project from a template" workflow within Pro, rather you are downloading the full project, and it can be modified as you see fit from there. Below are the recommended steps to take to deploy the template.Download the template anywhere on your PC by clicking the Download button on the top right below Sign In and Overview. This will download as a Zipped folder, likely to your Downloads folder.Go to the C drive of your computer and create a new folder called "Incidents", then create another folder within that Incidents folder with the name of the incident you are using the template for. For example, if the incident name is "Hurricane Lisa", the folder path should look something like "C:\Incidents\2024xxxx_HurricaneLisa".Extract the zipped folder contents from step 1 to that new incident folder you created in step 2. In the Hurricane Lisa example, data would be extracted to C:\Incidents\2024xxxx_HurricaneLisa.Go to the newly extracted folders and find the Projects folder. Open that and double click on any APRX file to begin work.

ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025Intended Environment: ArcGIS ProPurpose: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro and a spatial join of two live datasets.Description: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro. An associated ArcGIS Dashboard would then reflect these updates. Specifically, this Notebook would:First, pull two datasets - National Weather Updates and Public Schools - from the Living Atlas and add them to an ArcGIS Pro map.Then, the Notebook would perform a spatial join on two layers to give Public Schools features information on whether they fell within an ongoing weather event or alert. Next, the Notebook would truncate the Hosted Feature Service in ArcGIS Online - that is, delete all the data - and then append the new data to the Hosted Feature ServiceAssociated Resources: This Notebook was used as part of the demo for FedGIS 2025. Below are the associated resources:Living Atlas Layer: NWS National Weather Events and AlertsLiving Atlas Layer: U.S. Public SchoolsArcGIS Demo Dashboard: Demo Impacted Schools Weather DashboardUpdatable Hosted Feature Service: HIFLD Public Schools with Event DataNotebook Requirements: This Notebook has the following requirements:This notebook requires ArcPy and is meant for use in ArcGIS Pro. However, it could be adjusted to work with Notebooks in ArcGIS Online or ArcGIS Portal with the advanced runtime.If running from ArcGIS Pro, connect ArcGIS Pro to the ArcGIS Online or ArcGIS Portal environment.Lastly, the user should have editable access to the hosted feature service to update.

The market for GIS Mapping Tools is projected to reach a value of $XX million by 2033, growing at a CAGR of XX% during the forecast period (2025-2033). The market growth is attributed to the increasing adoption of GIS mapping tools by various industries, including government, utilities, and telecom, for a wide range of applications such as geological exploration, water conservancy projects, and urban planning. The convergence of GIS with other technologies such as artificial intelligence (AI) and the Internet of Things (IoT) is further driving market growth, as these technologies enable GIS mapping tools to provide more accurate and real-time data analysis. The market is segmented by type (cloud-based, web-based), application (geological exploration, water conservancy projects, urban planning, others), and region (North America, Europe, Asia Pacific, Middle East & Africa). North America is expected to remain the largest market for GIS mapping tools throughout the forecast period, due to the early adoption of these technologies and the presence of leading vendors such as Esri, MapInfo, and Autodesk. Asia Pacific is expected to experience the highest growth rate during the forecast period, due to the increasing adoption of GIS mapping tools in emerging economies such as China and India. Key industry players include Golden Software Surfer, Geoway, QGIS, GRASS GIS, Google Earth Pro, CARTO, Maptive, Shenzhen Edraw Software, MapGIS, Oasis montaj, DIVA-GIS, Esri, MapInfo, Autodesk, BatchGeo, Cadcorp, Hexagon, Mapbox, Trimble, and ArcGIS.

This map package includes the official Tribal areas that NTIA recognizes for their grant programs. The map package includes the following layers. Alaska Native Villages - this layer represents Alaska Native Villages and is created by Census. The layer was downloaded on Jan. 28, 2025, from here: TIGER/Line® Shapefiles.Native Hawaiian Areas - this layer represents Native Hawaiian Areas and is created by Census. The layer was downloaded on Jan. 28, 2025, from here: TIGER/Line® Shapefiles and has been filtered to only include Native Hawaiian Areas. BIA AIAN National LAR - this layer represents American Indian Lands and is created by the BIA. The layer was accessed here: BIA Access Open Data and was exported on Jan. 28, 2025. The layer was filtered to only include lands across the continental U.S. BIA AIAN LAR Supplemental - this layer is a supplemental dataset to the LAR. The layer was accessed here: BIA Access Open Data and was exported on Jan. 28, 2025.BIA AIAN Tribal Statistical Areas - this layer represents Tribal Statistical Areas located in Oklahoma. The layer was accessed here: BIA Access Open Data and was exported on Jan. 28, 2025.This map package was created on Jan. 28, 2025 and was created using ArcGIS Pro 3.4.0. If you have any questions regarding the map package please e-mail NTIAanalytics@ntia.gov.ResourcesCensus DataBIA Open DataBIA Data DisclaimerBy using this product, the user agrees to the below terms and conditions:No warranty is made by the Bureau of Indian Affairs (BIA) for the use of the data for purposes not intended by the BIA. This GIS Dataset may contain errors. There is no impact on the legal status of the land areas depicted herein and no impact on land ownership. No legal inference can or should be made from the information in this GIS Dataset. The GIS Dataset is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes. These data have been developed from the best available sources. Although efforts have been made to ensure that the data are accurate and reliable, errors and variable conditions originating from source documents and/or the translation of information from source documents to the systems of record continue to exist. Users must be aware of these conditions and bear responsibility for the appropriate use of the information with respect to possible errors, scale, resolution, rectification, positional accuracy, development methodology, time period, environmental and climatic conditions and other circumstances specific to these data. The user is responsible for understanding the accuracy limitations of the data provided herein. The burden for determining fitness for use lies entirely with the user. The user should refer to the accompanying metadata notes for a description of the data and data development procedures.Census Use RestraintsThe TIGER/Line Shapefile products are not copyrighted however TIGER/Line and Census TIGER are registered trademarks of the U.S. Census Bureau. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information in the TIGER/Line Shapefiles are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions. Coordinates in the TIGER/Line shapefiles have six implied decimal places, but the positional accuracy of these coordinates is not as great as the six decimal places suggest.

Source data found here: https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServerEach drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological SurveyPublication Date: January 7, 2025What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe 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 "Watershed Boundary Dataset" 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 "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.

Each drainage area is considered a Hydrologic Unit (HU) and is given a Hydrologic Unit Code (HUC) which serves as the unique identifier for the area. HUC 2s, 6s, 8s, 10s, & 12s, define the drainage Regions, Subregions, Basins, Subbasins, Watersheds and Subwatersheds, respectively, across the United States. Their boundaries are defined by hydrologic and topographic criteria that delineate an area of land upstream from a specific point on a river and are determined solely upon science based hydrologic principles, not favoring any administrative boundaries, special projects, or a particular program or agency. The Watershed Boundary Dataset is delineated and georeferenced to the USGS 1:24,000 scale topographic basemap.Hydrologic Units are delineated to nest in a multi-level, hierarchical drainage system with corresponding HUCs, so that as you move from small scale to large scale the HUC digits increase in increments of two. For example, the very largest HUCs have 2 digits, and thus are referred to as HUC 2s, and the very smallest HUCs have 12 digits, and thus are referred to as HUC 12s.Dataset SummaryPhenomenon Mapped: Watersheds in the United States, as delineated by the Watershed Boundary Dataset (WBD)Geographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands and American SamoaProjection: Web MercatorUpdate Frequency: AnnualVisible Scale: Visible at all scales, however USGS recommends this dataset should not be used for scales of 1:24,000 or larger.Source: United States Geological Survey (WBD)Data Vintage: January 7, 2025What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe 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 "Watershed Boundary Dataset" 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 "Watershed Boundary Dataset" in the search box, browse to the layer then click OK.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $39 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based GIS solutions offers enhanced accessibility, scalability, and cost-effectiveness, particularly appealing to smaller organizations. Secondly, the burgeoning need for precise spatial data analysis in various applications, including urban planning, geological exploration, and water resource management, significantly contributes to market growth. Thirdly, advancements in technologies such as AI and machine learning are integrating into GIS tools, leading to more sophisticated analytical capabilities and improved decision-making. Finally, the increasing availability of high-resolution satellite imagery and other geospatial data further fuels market expansion. However, market growth is not without challenges. High initial investment costs associated with implementing and maintaining sophisticated GIS systems can pose a barrier to entry for smaller businesses. Furthermore, the complexity of GIS software and the need for specialized skills to operate and interpret data effectively can limit widespread adoption. Despite these restraints, the market’s overall trajectory remains positive, with the cloud-based segment projected to maintain a dominant market share due to its inherent advantages. Growth will be geographically diverse, with North America and Europe continuing to be significant markets, while Asia-Pacific is expected to experience the fastest growth due to rapid urbanization and infrastructure development. The continued development of user-friendly interfaces and increased integration with other business intelligence tools will further accelerate market expansion in the coming years.

Geocode addresses for the Portland metropolitan region. This locator is an ArcGIS Pro version of the RLIS Address Locator, with autosuggestion capabilities enabled. It is based on RLIS data including the Master Address File and Streets and supports finding an address in a single-line format. It is available both as a geocode service and as a downloadable locator package. This is the new ArcGIS Pro based geocode service with autosuggest functionality enabled, the ArcMap-compatible version is available under the name "RLIS Address Locator." The new ArcGIS Pro version of the downloadable locator package is available under the name "RLIS Address Locator (Pro) - Download." Date of last data update: 2025-04-21 This is official RLIS data. Contact Person: Alicia Wood alicia.wood@oregonmetro.gov 503-813-7561 RLIS Metadata Viewer: https://gis.oregonmetro.gov/rlis-metadata/#/details/3736 RLIS Terms of Use: https://rlisdiscovery.oregonmetro.gov/pages/terms-of-use

This dataset represents the NDVI (Normalized Difference Vegetation Index) trend analysis for the Al Baha region, Saudi Arabia, covering the period from 2018 to 2025. The analysis is based on Sentinel-2 imagery, processed in Google Earth Engine (GEE) and ArcGIS Pro. NDVI trends calculated using annual mean values Small polygons < 0.005 km² removed Smoothed with a 500m tolerance Dissolved based on DN (Digital Number) classification Projection: UTM Zone 37N (EPSG: 32637)

1. Abstract Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information about landslide occurrence across the entire U.S. This data release is an update of previous versions 1 (Jones and others, 2019) and 2 (Belair and others, 2022). Changes relative to version 2 are summarized in us_ls_v3_changes.txt. It provides an integrated database of the landslides from these inventories (refer to US_Landslide_v3_gpkg) with a selection of uniform attributes, including links to the original digital inventory files (whenever available) (“Inv_URL”). The data release includes digital inventories created by both USGS and non-USGS authors. The original inventory is denoted by an abbreviation in the “Inventory” attribute. The full citation for each abbreviation can be found in us_ls_v3_references.csv. The date of the landslide event is included as a minimum and maximum (“Date_Min” and “Date_Max”) to accommodate events that happen within a range of dates. The date value is inherently difficult to interpret or discern due to the nature of landsliding, where some landslides move for long periods of time or move intermittently, and some areas can exhibit multiple landslide events. To preserve the constituent inventories as much as possible, we include all entries even if they are not considered landslides, such as “gullies” or “avalanche chutes.” We include a landslide type attribute when that information is available (“LS_Type”). The landslide classification system used in the original inventories is not always known or stated in the metadata, but many mapping entities use the schema from Cruden and Varnes (1996) or the updated schema from Hungr and others (2014). Given the wide range of landslide information sources in this data compilation, we provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide (entry) (“Confidence”). The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping. This confidence does not reflect a formal accuracy assessment of field attributes. Relative to the previous data releases (version 1 and 2), this update (v3) includes more inventories, updated confidence rules, a new landslide type attribute, a new unique identifier (“USGS_ID”), new machine-readable date fields, and an ancillary database containing all fields from the original inventories (refer to US_Landslide_v3_ancillary). Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort. When possible, please cite the constituent inventories as well as this data release. This data release includes: (1) a landslide point file and polygon file in multiple forms (US_Landslide_v3_gpkg, US_Landslide_v3_shp, US_Landslide_v3_csv), (2) an ancillary database with original fields (US_Landslide_v3_ancillary), (3) a spreadsheet that summarizes the confidence rules, their justification, and any extra analyses (us_ls_v3_analyses.csv), (4) a summary file of the changes made between version 2 and version 3 (us_ls_v3_changes.txt), (5) a file containing the references of the constituent inventories (us_ls_v3_references.csv), (6) and a readme (README.txt). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 2. Data fields Field Names Definitions USGS_ID Unique USGS identifier for each landslide entry. Date_Min Minimum possible date of landslide occurrence. If date is known to the day, Date_Min will have a value while Date_Max is empty. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Date_Max Maximum possible date of landslide occurrence. If date is known to the day, Date_Max will be empty while Date_Min has a value. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Fatalities Number of fatalities caused by landslide event. Confidence Confidence in landslide (entry) extent, nature, and location. LS_Type Landslide (entry) type. Classification schema of original inventories is often not specified. Inventory Name of original source inventory. Inv_URL URL or link to original source inventory. Info_Source Information source or sub-layer from original source inventory. Notes Unformatted notes field, includes additional information. Lat_N Latitude of point or polygon centroid in WGS 1984 Lon_W Longitude of point or polygon centroid in WGS 1984 3. Confidence attributes Confidence Definitions 1 Possible landslide (feature) in the area 2 Probable landslide (feature) in the area 3 Likely landslide (feature) at or near this location 5 Moderate confidence in extent or nature of landslide (feature) at this location 8 High confidence in extent or nature of landslide (feature) 4. References Belair, G.M., Jones, E.S., Slaughter, S.L., and Mirus, B.B., 2022, Landslide Inventories across the United States version 2: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZUX6N. Cruden, D.M. and Varnes, D.J., 1996, Landslide Types and Processes, in Turner, K.A. and Schuster R. L., eds., Landslides Investigation and Mitigation: Transportation Research Board, U.S. National Research Council Special Report 247, U.S. National Academy of Sciences, Chapter 3, p. 36-75. ESRI, 2023, ArcGIS Pro (Version 3.1.3), Redlands, CA: Environmental Systems Research Institute, Retrieved from https://www.esri.com/en-us/arcgis/products/arcgis-pro/resources. Hungr, O., Leroueil, S., and Picarelli, L., 2014, The Varnes classification of landslide types, an update, Landslides, 11(2), p. 167-194, https://doi.org/10.1007/s10346-013-0436-y. Jones, E.S., Mirus, B.B, Schmitt, R.G., Baum, R.L., Burns, W.J., Crawford, M., Godt, J.W., Kirschbaum, D.B., Lancaster, J.T., Lindsey, K.O., McCoy, K.E., Slaughter, S., and Stanley, T.A., 2019, Landslide Inventories across the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9E2A37P. Python Software Foundation, 2023, Python Language Reference, version 3.9, Retrieved from http://www.python.org. QGIS.org, 2022, QGIS Geographic Information System (Version 3.28.4-Firenze), QGIS Association, Retrieved from http://www.qgis.org.

One-eighth of the United States (247.3 million acres) is managed by the Bureau of Land Management. As part of the Department of the Interior, the agency oversees the 30 million acre National Landscape Conservation System, a collection of lands that includes 221 wilderness areas, 23 national monuments and 636 other protected areas. Bureau of Land Management Lands contain over 63,000 oil and gas wells and provide forage for over 18,000 grazing permit holders on 155 million acres of land. Dataset SummaryPhenomenon Mapped: United States lands managed by the Bureau of Land ManagementGeographic Extent: Contiguous United States and AlaskaData Coordinate System: WGS 1984Visible Scale: The data is visible at all scales but draws best at scales larger than 1:2,000,000.Source: BLM Surface Management Agency layer, Rasterized by Esri from features May 2025.Publication Date: December 2024This layer is a view of the USA Federal Lands layer. A filter has been used on this layer to eliminate non-Bureau of Land Management lands. For more information on layers for other agencies see the USA Federal Lands layer.What 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 "bureau of land management" 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 "bureau of land management" 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 shape file 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.

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.

Supplementary Data for Wueller et al. (2025): Science Objectives and Design Reference Mission to Rubin Crater on Mons Amundsen Near the South Pole of the Moon published in Advances in Space Research

Data contains shapefiles, layer packackges (ready for direct use in ArcGIS Pro), as well as a map plate of our geologic map that can be used in any geoinformation system (GIS).

If you use these data, please cite BOTH the Advances in Space Research publication and the Zenodo dataset.

Wueller, L., Xu, X., Iqbal, W., Brown, H., van der Bogert, C., Hu, T., Kang, Z., & Hiesinger, H. (2025). Supplementary Data for Wueller et al. (2025): Science Objectives and Design Reference Mission to Rubin Crater on Mons Amundsen Near the South Pole of the Moon [Data set]. In Advances in Space Research (Version v1). Zenodo. https://doi.org/10.5281/zenodo.15101821

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Structure

-> File "GIS" - It includes the shapefiles and raster data of the geologic map, landing sites, and traverses.

-> File "Layer_Package_ready_for_ArcGIS_Pro" - It includes all data (landing sites, boulders, traverses, and map) as layer package that can be directly used in ArcGIS Pro.

-> File "NAC_mosaics" - It contains both generated NAC mosaics of the study area. For more details, please see the publicatio in Advances in Space Research.

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For further questions contact lwueller@uni-muenster.de

Lukas Wueller, Institut für Planetologie, Universität Münster, Germany, March 28th 2025

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, considering the technological advancements and applications in agriculture (precision farming, crop monitoring), water conservancy (flood management, irrigation optimization), forest management (deforestation monitoring, resource assessment), and the public sector (urban planning, disaster response), a conservative estimate places the 2025 market size at approximately $2 billion. This figure reflects the substantial investments in satellite imagery acquisition and analysis capabilities worldwide. The market is further fueled by the rising adoption of cloud-based solutions, enhancing accessibility and scalability of software platforms. Trends such as the integration of AI and machine learning for automated image processing, the proliferation of high-resolution satellite imagery, and the increasing availability of open-source software are accelerating market expansion. However, factors such as the high cost of specialized software licenses and the need for skilled professionals to operate the sophisticated systems act as restraints. The market is segmented by application (agriculture, water conservancy, forest management, public sector, others) and software type (open-source, non-open-source). The North American and European markets currently hold significant shares, but the Asia-Pacific region is witnessing rapid growth due to increasing infrastructure development and government initiatives promoting geospatial technologies. This dynamic market landscape presents lucrative opportunities for both established players and emerging companies in the years to come. The forecast period (2025-2033) anticipates continued growth, with a projected CAGR of approximately 12%, driven by the aforementioned technological advancements and broadening applications across various industry verticals. The competitive landscape is comprised of both major players like ESRI, Trimble, and PCI Geomatica, offering comprehensive suites of software, and smaller, specialized companies focusing on niche applications or open-source solutions. The market is characterized by both proprietary and open-source software options. Open-source solutions like QGIS and GRASS GIS offer cost-effective alternatives, particularly for research and smaller organizations, while commercial solutions provide advanced functionalities and support. The increasing availability of cloud-based solutions is blurring the lines between these segments, with hybrid models emerging that combine the benefits of both. Future growth will be significantly influenced by collaborations between software providers and satellite imagery providers, fostering a more integrated ecosystem and streamlining the data acquisition and processing workflow. The market will continue to benefit from advancements in satellite technology, producing higher-resolution, more frequent, and more affordable imagery.

The Middle East Geospatial Analytics market, valued at $1.16 billion in 2025, is projected to experience robust growth, driven by significant investments in infrastructure development, smart city initiatives, and the burgeoning need for precise location intelligence across various sectors. A Compound Annual Growth Rate (CAGR) of 8.15% from 2025 to 2033 indicates a substantial expansion, with the market expected to surpass $2 billion by 2033. Key drivers include increasing adoption of advanced technologies like AI and machine learning within geospatial analytics, coupled with growing government initiatives promoting digital transformation and data-driven decision-making. The demand for accurate land management, resource optimization, and efficient urban planning is further fueling market expansion. Segmentation reveals strong growth in surface analysis and network analysis within the ‘By Type’ category, while the ‘By End-user Vertical’ segment is witnessing significant contributions from the Agriculture, Utility & Communication, and Defense & Intelligence sectors. The presence of established players like Esri, Bentley Systems, and Autodesk, alongside emerging specialized firms, ensures a competitive and dynamic market landscape. However, challenges like data security concerns, high implementation costs, and the need for skilled professionals could potentially restrain market growth. The Middle East's unique geopolitical landscape and rapid urbanization present both opportunities and challenges. Government initiatives focused on national infrastructure projects and sustainable development are creating substantial demand for geospatial analytics solutions. The region's focus on diversification beyond oil and gas is further stimulating adoption across sectors like agriculture, tourism, and transportation. However, regulatory hurdles and data privacy concerns, especially within the defense and intelligence sectors, need careful consideration. The high cost of sophisticated geospatial analytics technology and the need for specialized expertise might limit penetration in certain segments. Nevertheless, the long-term outlook remains optimistic, driven by the region's commitment to technological advancement and the increasing recognition of the value of data-driven insights for improved decision-making. Recent developments include: June 2023: Autodesk and Esri's partnership accelerated innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

During the 2024/2025 season, a number of containers converted to temporary living quarters / accommodation pods were installed on station by the trades staff under the supervision of the ESS. They were placed south of the plumbers workshop, inside the fence of North station. The vector line work representing the building footprints was digitised in ArcGIS pro from design drawings, and placed in 3D from offsets in the designs that were measured from on the ground features already contained within the AADC Enterprise GIS. The data is supplied as geo-packaged shapefiles.

Dieser Dienst enthält die Ladesäulen, die der Ladesäulenverordnung (LSV) genügen. Die Liste beinhaltet die Ladeeinrichtungen aller Betreiberinnen und Betreiber, die das Anzeigeverfahren der Bundesnetzagentur vollständig abgeschlossen und einer Veröffentlichung im Internet zugestimmt haben. Die Zahl der öffentlich zugänglichen Ladeeinrichtungen in Deutschland ist daher größer als hier dargestellt.Zusätzliche InformationLadesäulen oder Ladestationen dienen dem Laden von Elektrofahrzeugen. Sie sind konventionellen Zapfsäulen nachempfunden und bieten in der Regel verschiedene Kabelverbindungen. Die Punkte der Ladesäulen wurden mit den Koordinaten aus der BnA-Tabelle erstellt. In manchen Fällen stimmen die Koordinaten nicht mit der Adresse überein. Quelle Bundesnetzagentur - E-Mobilität | Zuletzt aufgerufen am 30.06.2025 VerarbeitungsprozessDie Daten wurden mit ArcGIS Pro auf WGS84 Web Mercator umprojiziert. Die Punkte wurden mit den Koordinaten aus der BnA-Tabelle erstellt. In manchen Fällen stimmen die Koordinaten nicht mit der Adresse überein. Datenbestand2024