The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

Statewide 2016 Lidar points colorized with 2018 NAIP imagery as a scene created by Esri using ArcGIS Pro for the entire State of Connecticut. This service provides the colorized Lidar point in interactive 3D for visualization, interaction of the ability to make measurements without downloading.Lidar is referenced at https://cteco.uconn.edu/data/lidar/ and can be downloaded at https://cteco.uconn.edu/data/download/flight2016/. Metadata: https://cteco.uconn.edu/data/flight2016/info.htm#metadata. The Connecticut 2016 Lidar was captured between March 11, 2016 and April 16, 2016. Is covers 5,240 sq miles and is divided into 23, 381 tiles. It was acquired by the Captiol Region Council of Governments with funding from multiple state agencies. It was flown and processed by Sanborn. The delivery included classified point clouds and 1 meter QL2 DEMs. The 2016 Lidar is published on the Connecticut Environmental Conditions Online (CT ECO) website. CT ECO is the collaborative work of the Connecticut Department of Energy and Environmental Protection (DEEP) and the University of Connecticut Center for Land Use Education and Research (CLEAR) to share environmental and natural resource information with the general public. CT ECO's mission is to encourage, support, and promote informed land use and development decisions in Connecticut by providing local, state and federal agencies, and the public with convenient access to the most up-to-date and complete natural resource information available statewide.Process used:Extract Building Footprints from Lidar1. Prepare Lidar - Download 2016 Lidar from CT ECO- Create LAS Dataset2. Extract Building Footprints from LidarUse the LAS Dataset in the Classify Las Building Tool in ArcGIS Pro 2.4.Colorize LidarColorizing the Lidar points means that each point in the point cloud is given a color based on the imagery color value at that exact location.1. Prepare Imagery- Acquire 2018 NAIP tif tiles from UConn (originally from USDA NRCS).- Create mosaic dataset of the NAIP imagery.2. Prepare and Analyze Lidar Points- Change the coordinate system of each of the lidar tiles to the Projected Coordinate System CT NAD 83 (2011) Feet (EPSG 6434). This is because the downloaded tiles come in to ArcGIS as a Custom Projection which cannot be published as a Point Cloud Scene Layer Package.- Convert Lidar to zlas format and rearrange. - Create LAS Datasets of the lidar tiles.- Colorize Lidar using the Colorize LAS tool in ArcGIS Pro. - Create a new LAS dataset with a division of Eastern half and Western half due to size limitation of 500GB per scene layer package. - Create scene layer packages (.slpk) using Create Cloud Point Scene Layer Package. - Load package to ArcGIS Online using Share Package. - Publish on ArcGIS.com and delete the scene layer package to save storage cost.Additional layers added:Visit https://cteco.uconn.edu/projects/lidar3D/layers.htm for a complete list and links. 3D Buildings and Trees extracted by Esri from the lidarShaded Relief from CTECOImpervious Surface 2012 from CT ECONAIP Imagery 2018 from CTECOContours (2016) from CTECOLidar 2016 Download Link derived from https://www.cteco.uconn.edu/data/download/flight2016/index.htm

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.Lidar data have become an important source for detailed 3D information for cities as well as forestry, agriculture, archaeology, and many other applications. Topographic lidar surveys, which are conducted by airplane, helicopter or drone, produce data sets that contain millions or billions of points. This can create challenges for storing, visualizing and analyzing the data. In this tutorial you will learn how to create a LAS Dataset and explore the tools available in ArcGIS Pro for visualizing lidar data.To download the tutorial and data folder, click the Open button to the top right. This will download a ZIP file containing the tutorial documents and data files.Software & Solutions Used: ArcGIS Pro Advanced 3.x. Last tested with ArcGIS Pro version 3.3. Time to Complete: 30 - 60 minsFile Size: 337 MBDate Created: August 2020Last Updated: March 2024

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

The distribution map of Festuca dolichophylla relies on diverse data sources. Geographical coordinates (latitude and longitude) and country initials (countryCode) were extracted from Tropicos, the Gbif repository (up to May 2019), and the iDigBio database (up to July 2021). Additionally, data from other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999), were integrated. The Gbif data points are associated with gbifID numbers for reference. Please note that this compilation provides essential information for understanding the distribution of F. dolichophylla across various regions.

Software

Organized data by geographic coordinates was uploaded to ArcGIS Pro v. 3.2.0 for map production. Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.

Methods

The dataset for the distribution map of Festuca dolichophylla was meticulously collected from various sources.

Data Collection:

Tropicos: Data were extracted from Tropicos until December 2023.

Gbif Repository: Data was sourced from the Gbif repository until May 2019.

iDigBio Database: Additional data points were retrieved from the iDigBio database up to July 2021.

Other Sources: We also incorporated data from various other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999).

Data Organization and Processing:

All collected data points were meticulously organized by coordinates.

We ensured consistency by cross-referencing and validating the data.

The dataset was then uploaded to ArcGIS Pro v. 3.2.0 for map production.

Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.

Funding

Neotropical Grassland Conservancy, Award: Memorial grant 2020

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.The model is trained with classified LiDAR that follows the The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

Monroe County, in southeastern West Virginia, hosts world-class karst within carbonate units of Mississippian and Ordovician age. There are at least 412 known caves in the county. Location data for these caves were collected from the West Virginia Speleological Survey (WVASS) Bulletin 22 (Dasher, 2019). Point features were created in ArcGIS Pro for each cave location and were used to make a point density raster. This raster displays the number of cave points per square kilometer.

2022 Douglas County building footprints. This data was created using QL1 LiDAR classified point cloud (.LAS) captured by VeriDaas between April 4-17th 2022. ArcGIS Pro 3.x was used to process the 2022 building footprints. After the footprints were created, all polygons less than 64 square feet were deleted. All larger buildings that were inside a commercial or industrial parcel were examined to make sure HVAC artifacts were removed from the building footprint.

Methods:This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution hillshade raster was produced from the 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hillshade derived from DTM using the ESRI Spatial Analyst ‘hillshade’ function The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The hillshade provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

Dataset description: This repository contains data pertaining to the manuscript "Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system." submitted to Journal of Maps. NOAH-H Mosaics: Mawrth_Vallis_NOAHH_Mosaic_DC_IG_25cm4bit_20230121_reclass.zip This folder contain mosaics of terrain classifications for Mawrth Vallis, Mars, made by the Novelty or Anomaly Hunter - HiRISE (NOAH-H) deep learning convolutional neural network developed for the European Space Agency (ESA) by SCISYS Ltd. In coordination with the Open University Planetary Environments Group. These folders contain the NOAH-H mosaics, as well as ancillary files needed to display the NOAH-H products in geographic information software (GIS). Included are two large raster datasets, containing the NOAH-H classification for the entire study area. One uses the 14 descriptive classes of the terrain, and the other with the five interpretative groups (Barrett et al., 2022). · Mawrth_Vallis_NOAHH_Mosaic_DC_25cm4bit_20230121_reclass.tif Contains the full 14 class “Descriptive Classes” (DC) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. · Mawrth_Vallis_NOAHH_Mosaic_IG_25cm4bit_20230121_reclass.tif Contains the 5 class “Interpretive Groups” (IG) dataset, reclassified so that pixel values reflect the original NOAH-H ontology, and not the priority rankings described in Wright et al., (2022) and Barrett et al., (2022b). It is accompanied by all auxiliary files required to view the data in GIS. Symbology layer files: NOAH-H_Symbology.zip This folder contains GIS layer file and colour map files for both the Descriptive Classes (DC) and interpretive Groups (IG) versions of the classification. These can be applied to the data using the symbology options in GIS. Georeferencing Control points: Mawrth_Vallis_Final_Control_Points.zip This file contains the control points used to georeferenced the 26 individual HiRISE images which make up the mosaic. These allow publicly available HiRISE images to be aligned to the terrain in Mawrth Vallis, and thus the NOAH-H Mosaic. Twenty-six 25 cm/pixel HiRISE images of Mawrth Vallis were used as input for NOAH-H. These are:

PSP_002140_2025_RED

PSP_002074_2025_RED

ESP_057351_2020_RED

ESP_053909_2025_RED

ESP_053698_2025_RED

ESP_052274_2025_RED

ESP_051931_2025_RED

ESP_051351_2025_RED

ESP_051219_2030_RED

ESP_050217_2025_RED

ESP_046960_2025_RED

ESP_046670_2025_RED

ESP_046525_2025_RED

ESP_046459_2025_RED

ESP_046314_2025_RED

ESP_045536_2025_RED

ESP_045114_2025_RED

ESP_044903_2025_RED

ESP_043782_2025_RED

ESP_043637_2025_RED

ESP_038758_2025_RED

ESP_037795_2025_RED

ESP_037294_2025_RED

ESP_036872_2025_RED

ESP_036582_2025_RED

ESP_035804_2025_RED NOAH-H produced corresponding 25 cm/pixel rasters where each pixel is assigned a terrain class based on the corresponding pixels in the input HiRISE image. To mosaic the NOAH-H rasters together, first the input HiRISE images were georeferenced to the HRSC basemap (HMC_11E10_co5) tile, using CTX images as an intermediate step. High order (spline, in ArcGIS Pro 3.0) transformations were used to make the HiRISE images georeference closely onto the target layers. Once the HiRISE images were georeferenced, the same control points and transformations were applied to the corresponding NOAH-H rasters. To mosaic the georeferenced NOAH-H rasters the pixel values for the classes needed to be changed so that more confidently identified, and more dangerous, classes made it into the mosaic (see dataset manuscript for details. To produce a HiRISE layer which fits the NOAH-H classification, download one of the listed HiRISE images from https://www.uahirise.org/, Select the corresponding control point file from this archive and apply a spline transformation through the GIS georeferencing toolbar. Manually Mapped Aeolian Bedforms: Mawrth_Manual_TARs.zip The manually mapped data was produced by Fawdon, independently of the NOAH-H project, as an assessment of “Aeolian Hazard” at Mawrth Vallis. This was done to inform the ExoMars landing site selection process. This file contains two GIS shape files, containing the manually mapped bedforms for both the entire mapping area, and the HiRISE image ESP_046459_2025_RED where the two datasets were compared on a pixel scale. The full manual map is offset slightly from the NOAH-H, since it was digitised from bespoke HiRISE orthomosaics, rather than from the publicly available HiRISE Red band images. It is suitable for comparison to the NOAH-H data with 100m-1km aggregation as in figure 8 of the associated paper. It is not suitable for pixel scale comparison. The map of ESP_046459_2025_RED was manually georeferenced to the NOAH-H mosaic, allowing for direct pixel to pixel comparisons, as presented in figure 6 of the associated paper. Two GIS shape files are included: · Mawrth_Manual_TARs_ESP_046459_2025.shp · Mawrth_Manual_TARs_all.shp Containing the high fidelity data for ESP_046459_2025, and the medium fidelity data for the entire area respectively. The are accompanied by ancillary files needed to view them in GIS. Gridded Density Statistics This dataset contains gridded density maps of Transverse Aeolian Ridges and Boulders, as classified by the Novelty or Anomaly Hunter – HiRISE (NOAH-H). The area covered is the runner up candidate ExoMars landing site in Mawrth Vallis, Mars. These are the data shown in figures; 7, 8, and S1. Files are presented for every classified ripple and boulder class, as well as for thematic groups. These are presented as .shp GIS shapefiles, along with all auxiliary files required to view them in GIS. Gridded Density stats are available in two zip folders, one for NOAH-H predicted density, and one for manually mapped density. NOAH-H Predicted Density: Mawrth_NOAHH_1km_Grid_TAR_Boulder_Density.zip Individual classes are found in the files: · Mawrth_NOAHH_1km_Grid_8TARs.shp · Mawrth_NOAHH_1km_Grid_9TARs.shp · Mawrth_NOAHH_1km_Grid_11TARs.shp · Mawrth_NOAHH_1km_Grid_12TARs.shp · Mawrth_NOAHH_1km_Grid_13TARs.shp · Mawrth_NOAHH_1km_Grid_Boulders.shp Where the text following Grid denotes the NOAH-H classes represented, and the landform classified. E.g. 8TARs = NOAH-H TAR class 8. The following thematic groups are also included: · Mawrth_NOAHH_1km_Grid_8_11continuousTARs.shp · Mawrth_NOAHH_1km_Grid_12_13discontinuousTARs · Mawrth_NOAHH_1km_Grid_8_10largeTARs.shp · Mawrth_NOAHH_1km_Grid_11_13smallTARs.shp · Mawrth_NOAHH_1km_Grid_8_13AllTARs.shp When the numbers denote the range of NOAH-H classes which were aggregated to produce the map, followed by a description of the thematic group: “continuous”, “discontinuous”, “large”, “small”, “all”. Manually Mapped Density Plots: Mawrth_Manual_1km_Grid.zip These GIS shapefiles have the same format as the NOAH-H classified ones. Three datasets are presented for all TARs (“_allTARs”), Continuous TARs (“_con”) and Discontinuous TARs (“_dis”) · Mawrth_Manual_1km_Grid_AllTARs.shp · Mawrth_Manual_1km_Grid_Con.shp · Mawrth_Manual_1km_Grid_Dis.shp Related public datasets: The HiRISE images discussed in this work are publicly available from https://www.uahirise.org/. and are credited to NASA/JPL/University of Arizona. HRSC images are credited to the European Space Agency; Mars Express mission team, German Aerospace Center (DLR), and the Freie Universität Berlin (FUB). They are available at the ESA Planetary Science Archive (PSA) https://www.cosmos.esa.int/web/psa/mars-express and are used under the Creative Commons CC BY-SA 3.0 IGO licence. SPATIAL DATA COORDINATE SYSTEM INFORMATION All NOAH-H files and derivative density plots have the same projected coordinate system: “Equirectangular Mars” - Projection: Plate Carree - Sphere radius: 3393833.2607584 m SOFTWARE INFORMATION All GIS workflows (georeferencing, mosaicking) were conducted in ArcGIS Pro 3.0. NOAH-H is a deep learning semantic segmentation software developed by SciSys Ltd for the European Space Agency to aid preparation for the ExoMars rover mission.

The classification of point cloud datasets to identify distribution wires is useful for identifying vegetation encroachment around power lines. Such workflows are important for preventing fires and power outages and are typically manual, recurring, and labor-intensive. This model is designed to extract distribution wires at the street level. Its predictions for high-tension transmission wires are less consistent with changes in geography as compared to street-level distribution wires. In the case of high-tension transmission wires, a lower ‘recall’ value is observed as compared to the value observed for low-lying street wires and poles.Using the modelFollow the guide to use the model. The model can be used with ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with point geometry (X, Y and Z values). Note: The model is not dependent on any additional attributes such as Intensity, Number of Returns, etc. This model is trained to work on unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: Classcode Class Description 0 Background Class 14 Distribution Wires 15 Distribution Tower/PolesApplicable geographiesThe model is expected to work within any geography. It's seen to produce favorable results as shown here in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the RandLANet model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Background (0) 0.999679 0.999876 0.999778 Distribution Wires (14) 0.955085 0.936825 0.945867 Distribution Poles (15) 0.707983 0.553888 0.621527Training dataThis model is trained on manually classified training dataset provided to Esri by AAM group. The training data used has the following characteristics: X, Y, and Z linear unitmeter Z range-240.34 m to 731.17 m Number of Returns1 to 5 Intensity1 to 4095 Point spacing0.2 ± 0.1 Scan angle-42 to +35 Maximum points per block20000 Extra attributesNone Class structure[0, 14, 15]Sample resultsHere are a few results from the model.

A relative exposure index (REI), unweighted fetch, effective fetch, and other fetch-based indices (i.e., sum, minimum) were calculated for the Newfoundland and Labrador (NL) Shelves bioregion. Due to the extensive coastline of the study region, this analysis was conducted for a 5km buffered region along the coast at a spatial resolution of 250m. Detailed methods on the selection of input points for the NL bioregion are included below. Methods Preprocessing and input point selection: Land boundary files were obtained for Eastern Canada and the Canadian Arctic (NrCan 2017) at a scale of 1:50,000 as well as for Saint Pierre and Miquelon (Hijmans 2015), and the New England states (GADM 2012) however the scale at which these layers were produced is unknown. Land boundary files were merged into a single land polygon layer and watercourses reaching for in-land and/or above sea level were clipped from this polygon layer (Greyson 2021). A 5km buffer was generated around the NL provincial boundary. This buffer was then clipped by all land polygons to remove areas overlapping land polygons within the study area. All buffer segments intersecting the NAFO divisions within the NL bioregion were selected and the Union tool in ArcGIS Pro (v. 2.7.2) was used to fill-in gaps within the buffered area, creating a more continuous polygon. The buffered layer was then dissolved, and the NL provincial boundary polygon was erased from the buffered layer to create the study area polygon. A 250m fishnet was created and clipped to the study area (5km buffer layer) and the feature to point tool was used (with the “inside parameter checked”) to convert this grid into a point layer (approx. 1,000,000 points). The spatial resolution for all subsequent analyses was matched to the fishnet grid at 250m. References GADM database of Global Administrative Areas (2012). Global Administrative Areas, version 2.0. (accessed 2 December 2020). www.gadm.org Greyson, P (2021) Land boundary file for Eastern Canada, the Canadian Arctic, the New England States and Saint Pierre and Miquelon. [shapefile]. Unpublished data. Hijmans, R. and University of California, Berkeley, Museum of Vertebrate Zoology. (2015). First-level Administrative Divisions, Saint Pierre and Miquelon, 2015. UC Berkeley, Museum of Vertebrate Zoology. Available at: http://purl.stanford.edu/bz573nv9230 Natural Resources Canada (2017) Administrative Boundaries in Canada - CanVec Series - Administrative Features - Open Government Portal. (accessed 2 December 2020). https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97.

Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool. The address service contains statewide address points and related landmark name alias table and street name alias table.The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

Statewide Download (FGDB) (SHP)Users can also download smaller geographic areas of this feature service in ArcGIS Pro using the Copy Features geoprocessing tool. The address service contains statewide address points and related landmark name alias table and street name alias table.The New Jersey Office of Information Technology, Office of GIS (NJOGIS), in partnership with several local GIS and public safety agencies, has built a comprehensive statewide NG9-1-1 database meeting and exceeding the requirements of the National Emergency Number Association (NENA) 2018 NG9-1-1 GIS Data Standard (NENA-STA-006.1-2018). The existing New Jersey Statewide Address Point data last published in 2016 has been transformed in the NENA data model to create this new address point data.The initial address points were processed from statewide parcel records joined with the statewide Tax Assessor's (MOD-IV) database in 2015. Address points supplied by Monmouth County, Sussex County, Morris County and Montgomery Township in Somerset County were incorporated into the statewide address points using customized Extract, Transform and Load (ETL) procedures.The previous version of the address points was loaded into New Jersey's version of the NENA NG9-1-1 data model using Extract, Transform and Load (ETL) procedures created with Esri's Data Interoperability Extension. Subsequent manual and bulk processing corrections and additions have been made, and are ongoing.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.

This dynamic imagery layer features Landsat 8 and Landsat GLS imagery, rendered on-the-fly as Short-wave Infrared with DRA, for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.

Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Short-wave Infrared (bands 7,6,4) with Dynamic Range Adjustment (DRA).Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Multispectral BandsThe table below lists all available multispectral OLI bands. Short-wave Infrared with DRA consumes bands 7,6,4.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic app is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.

*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

The amount of water in soil is based on rainfall amount, what proportion of rain infiltrates into the soil, and the soil"s storage capacity. Available water storage is the maximum amount of plant available water a soil can provide. It is an indicator of a soil’s ability to retain water and make it sufficiently available for plant use. Available Water Storage is a capacity estimate for the top 150 centimeters of soil. It is calculated from the difference between soil water content at field capacity and the permanent wilting point adjusted for salinity and fragments. Available water storage is used to develop water budgets, predict droughtiness, design and operate irrigation systems, design drainage systems, protect water resources, and predict yields. Available water storage is an important input into hydrologic models including the Soil and Water Assessment Tool (SWAT) - a water quality model that is designed to assess non-point and point source pollution at the river basin scale. Available water storagecan also be used as an indication of a soil"s drought susceptibility, for water recharge modeling, to assess a soil"s ability to support crops, and for many other purposes. Dataset SummaryPhenomenon Mapped: Amount of water a soil can hold that is available to plantsGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: MillimetersCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource:Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for available water storage is derived from the gSSURGO map unit aggregated attribute table field: Available Water Storage 0-150cm Weighted Average (aws0150wta). 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 "available water storage" 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 "available water storage" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions. 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.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at https://goto.arcgisonline.com/landscape11/USA_Soils_Available_Water_Storage.The amount of water in soil is based on rainfall amount, what proportion of rain infiltrates into the soil, and the soil's storage capacity. Available water storage is the maximum amount of plant available water a soil can provide. It is an indicator of a soil’s ability to retain water and make it sufficiently available for plant use. Available Water Storage is a capacity estimate for the top 150 centimeters of soil. It is calculated from the difference between soil water content at field capacity and the permanent wilting point adjusted for salinity and fragments.Available water storage is used to develop water budgets, predict droughtiness, design and operate irrigation systems, design drainage systems, protect water resources, and predict yields. Available water storage is an important input into hydrologic models including the Soil and Water Assessment Tool (SWAT) - a water quality model that is designed to assess non-point and point source pollution at the river basin scale. Available water storage can also be used as an indication of a soil's drought susceptibility, for water recharge modeling, to assess a soil's ability to support crops, and for many other purposes.Dataset SummaryPhenomenon Mapped: Amount of water a soil can hold, that is available to plantsUnits: MillimetersCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: July 2020ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for available water storage is derived from the gSSURGO map unit aggregated attribute table field Available Water Storage 0-150cm Weighted Average (aws0150wta).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 "available water storage" 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 "available water storage" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.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.

This layer maps emerging hot spots of statistically significant areas of forest loss aggregated into 1-mile bins, by year from 2001-2017. This layer was created by running the Emerging Hot Spot Analysis tool in ArcGIS pro using Hansen Forest Loss data for the Thrive region. That data was converted from raster to vector and converted to points. The points data was used to create a space-time cube, used to map changing and persistent hot spots of forest loss over time. This data does not take into account forestry practices or weather events for the region.

Version 3 of the Named Landforms of the World (NLWv3) is an update of version 2 of the Named Landforms of the World (NLWv2). NLWv2 will remain available as the compilation that best matches the work of E.M. Bridges and Richard E. Murphy. In NLWv3, we added attributes that describe each landform's volcanism based on data from the Smithsonian Institution's Global Volcanism Program (GVP). We designed NLWv3 layers for two purposes:Label maps with broadly accepted names for physiographic features. Use the polygons as a basis to add fields (attributes) to observation data or other small features to facilitate rich and relevant descriptions that indicate how other features relate to named physiographic features. Three workflows are recommended: (1) For point features, Identity and then Join Field; (2) Zonal Statistics as Table and then Join Field, and when many such attributes are being produced, (3) when adding multiple different attributes, the recently added Zonal Characterization tool and then Join Field. While we gained ability to estimate the area of Earth"s volcanic landforms, we also learned that volcanoes are relatively short-lived as landforms. The GVP provided two inventories, one for the Holocene Epoch, which is the most recent 11,700 years (since the last ice age), and for the Pleistocene Epoch, which precedes the Holocene, and lasted about 2.6 million years. There were only 7.8% more volcanoes included for the Pleistocene, even though the Pleistocene is 222 times longer. That means most older volcanoes have disappeared through natural erosional and depositional processes. In the NLWv3, we consider volcanic landforms as being one of many types of landforms, including calderas, clusters and complexes, shields, stratovolcanoes, or minor volcanic features such as lava domes and fissure vents. Not all of the GVP features, particularly fissure vents and remnants of calderas, are large enough to be mapped as polygons in the NLWv3. Similarly, complexes and volcanic fields typically had greater areas and included many individual cinder cones and calderas. ContinentCount of Volcanic LandformsArea km2 of Volcanic Landforms (% of land area)Europe7822,888 (0.23%)Antarctica4234,035 (0.27%)Australia14757,422 (0.65%)South America37081,475 (0.46%)Small Volcanic Islands559124,310 (8.52%)Africa282147,116 (0.50%)Asia698227,486 (0.53%)North America622295,340 (1.23%)Global Totals2,7981,000,073 (0.67%) Overview of UpdatesCorresponding landform polygons were assigned attributes for the GVP"s ID, name, province, and region. See details in the volcanic attributes section below. Additionally, an describing volcanism for each GVP feature was derived from these and several other GVP attributes to provide a reader-friendly characterization of each feature.Landforms of Antarctica. Given recent analysis of Antarctica and the GVP data it became possible to provide rudimentary landform features for Antarctica. See details in the Antarctica section below.Refined the definition of Murphy"s Isolated Volcanics classification. If the volcanic landform occurred outside of a orogenic, rifting, or subducting zone, it could not be considered isolated, as this is where volcanos are expected to occur. Only volcanoes occurring in areas with no tectonic activity are considered Isolated Volcanics, and these typically occur in mid-continent or mid-tectonic plate. See details in the Isolated Volcanic Areas section below.Edits to tectonic process attributes in selected areas. The Global Volcanism Program point locations for volcanoes includes an attribute for the underlying tectonic process. The concept matched the existing tectonic process in the NLWv2 and we compared the values. When the values differed, we reviewed research and made changes. See details in the Tectonic Process section below.Minor boundary changes at the province and lower levels in the western mountains of North and South America. See details in the Boundary Change Locations section below.Technical CharacteristicsThe NLWv2 and NLWv3 are derived the same raster datasets used to produce the 2018 version of the World Terrestrial Ecosystems (WTEs), which when combined have a lowest-common-denominator resolution, a.k.a. minimum-mapping-unit of 1-km. This means that some features, such as small islands are not included and complex coastlines are simplified and only included as land if the 1-km cell contains at least 50% land. Because the coastlines included in the original datasets varied by as much as 3-km from the actual coastline, nearly always due to missing land, we manually corrected many of the worst cases in NLWv2 using the 12 to 30-meter resolution World Hillshade layer as a guide. In NLWv3, we continued this work by adding 247 volcanic islands, some of which were smaller than 1-km in area. We estimate these islands to have been about one percent of the smaller islands of the world. In NLWv3, we also refined the coastlines of volcanic coastal areas, particularly in Oceania and Japan. For NLWv4, we plan to continue this refinement work intending that future versions of NLW will have a progressively refined, medium resolution coastline, though we do not intend to capture the full detail of the Global Islands dataset produced from 30-m Landsat. Detailed Description of Updates Volcanic AttributesWe combined the Holocene and Pleistocene spreadsheets containing the coordinates and attributes for each volcano, then added a column for the geologic age before exporting as a .CSV file and importing into ArcGIS Pro. We used the XY Table to Points tool to create point features. We ultimately found that nearly ten percent of the point locations lacked sufficient precision to fall within the correct landform polygon, so we manually reviewed each point and assigned the Volcano ID to each polygon.We were able to assign 2,394 of 2,662 GVP volcanic features to landform polygons. 198 GVP features were not used because they represented undersea features and 75 GVP features did not have apparent landforms; either being very small or indistinguishable from surrounding topography. Of the 2,394 assigned GVP features, 48% are Holocene age features and 52% are Pleistocene age features. We found that 225 GVP features were not located within a landform feature that topographically represented a volcanic landform feature, e.g., a caldera or stratovolcano. This was usually due to insufficient precision of the coordinates provided, which sometimes were rounded to the nearest integer of latitude and longitude and could be over 50-km distant from the landform"s location. AttributeDescriptionVolcano ID (SI)The six-digit unique ID for the Global Volcanism Program features.Volcano Name (SI)The Name of the volcanic feature as provided by the Global Volcanism Program. Volcanic Region (SI)The Name of the volcanic region as provided by the Global Volcanism Program. Volcanic Province (SI)The Name of the volcanic province as provided by the Global Volcanism Program. VolcanismA consistently formatted description volcanism for the landform feature based on the age, last eruption, landform type, and type of material. This information was not consistently available from the Global Volcanism Program, and we used a Python script to determine the condition of the Global Volcanism Program"s data and then include whatever information was available. AntarcticaSeveral recent analyses of Antarctica complemented the GVP point features. In particular, the British Antarctic Survey"s 2019 Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet shows sufficiently detailed land surface elevation beneath the ice sheets to support identifying topographic landform classes. We georeferenced the elevation image and combined that with Bridge"s geomorphological divisions and provinces to divide the continent into landforms. More work needs to be done to make these landform polygons as rich and accurately defined as those in NLWv2. Isolated Volcanic AreasNLWv2 has 333 Isolated Volcanic landforms. We intentionally expanded on Murphy"s map which could not show many of the smaller landforms and areas due to the 1:50,000,000 scale (poster sized map of the world). Murphy"s map only included isolated volcanic areas in three locations: north-central Africa, Hawaii, and Iceland. In NLWv2, we used the Global Lithological Map to identify several areas on each continent and used the example of Hawaii to include many other known volcanic islands. In most ways, Isolated Volcanics denoted geographic isolation from other mountain systems. NLWv3 contains 2,798 volcanic landform features, and 185 have Murphy"s Isolated Volcanic structure class because they do not occur within a region with the tectonic process of orogenic, subduction, or rifting. These Isolated Volcanic landform features are located mostly in mid-tectonic plate regions of Africa, the Arabian Peninsula, and on islands, particularly in the southern hemisphere, with a few in North America and Asia. NLWv3 contains 2,603 volcanic landform features, occurring on all continents and islands within all oceans. Tectonic ProcessThe GVP data included a tectonic setting attribute that was compiled independently of the NLWv2 tectonic setting variable. When these differed, we reviewed and if needed update the tectonic setting variable in the NLWv3. This also exposed several regions of landforms requiring updates to the Structure class. These areas included Japan, northeast Asia, the Aleutian Islands, and Alaska to either Orogenic or None. We independently verified these regions using Orogeny and Mantle Dynamics: role of tectonic erosion and second continent in the mantle transition zone which indicated specific orogenic and subducting areas, disagreeing with our original assessment and the GVP attribution for tectonic setting. Tectonic ProcessHolocene Volcanic Features Pleistocene Volcanic FeaturesNone (Isolated)7797Orogenic329497Subduction