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.

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

Raw lidar data consist of positions (x, y) and intensity values. They must undergo a classification process before individual points can be identified as belonging to ground, building, vegetation, etc., features. By completing this tutorial, you will become comfortable with the following skills:Converting .zlas files to .las for editing,Reassigning LAS class codes,Using automated lidar classification tools, andUsing 2D and 3D features to classify lidar data.Software Used: ArcGIS Pro 3.3Time to Complete: 60 - 90 minutesFile Size: 57mbDate Created: September 25, 2020Last Updated: September 27, 2024

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/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

These data are a surface water classification map of surface water in the riparian corridor of Grand Canyon between Glen Canyon and Pearce Ferry, Arizona, published in ESRI shapefile format. The map was classified from 0.2 m resolution, multispectral imagery (Sankey and others, 2024) and are the same spatial resolution as the imagery. In order to differentiate between the boundary between each river reach in Grand Canyon, the map is categorized with a water channel name, including the mainstem Colorado River or other major tributaries by name. Data analyses were performed using ENVI V.5.6.1 and IDL V8.8.1, a registered trademark of NV5 Global, Inc. and ArcGIS PRO 3.3.1, a product of Esri, Inc.

These contour lines were derived and delivered for Pennsylvania from the PAMAP Quality Level 3 (QL3) LIDAR data collection between 2006 and 2008. Some post-processing has been done to the original deliverables, including merging, line smoothing, and eliminating duplicate (overlapping) data between collections. This dataset renders the contour lines with the following scale-dependent visibility: 100 foot increments between 1:200,000 and 1:100,000 | 50 foot increments between 1:100,000 and 1:30,000 | 20 foot increments between 1:30,000 and 1:5,000 | 10 foot increments between 1:5,000 and 1:1,000 | and 2 foot increments between 1:1,000 and 1:10. The lines have been smoothed using the ArcGIS Pro 3.3 Smooth Line geoprocessing tool via the Polynomial Approximation with Exponential Kernal (PAEK) and setting a 10 ft smoothing tolerance distance. The extent of this data extends slightly beyond the Pennsylvania boundary into all surrounding states to ensure complete coverage of Pennsylvania. Duplicate (overlapping) contour data between collection years and north/south state plane zones has been eliminated by splitting the data from adjacent collects at county boundaries to ensure a seamless product with no duplication or overlapping data. The contour line geometries along the county boundaries that separate different years of PAMAP data collection (2006, 2007, and 2008) do not always connect properly.

GDB Version: ArcGIS Pro 3.3Additional Resources:Shapefile DownloadShapefile Download (Clipped to VIMS shoreline)Administrative Boundary Data Standard REST Endpoint (Unclipped) - REST Endpoint (Clipped)The Administrative Boundary feature classes represent the best available boundary information in Virginia. VGIN initially sought to develop an improved city, county, and town boundary dataset in late 2013, spurred by response of the Virginia Administrative Boundaries Workgroup community. The feature class initially started from an extraction of features from the Census TIGER dataset for Virginia. VGIN solicited input from localities in Virginia through the Road Centerlines data submission process as well as through public forums such as the Virginia Administrative Boundaries Workgroup and VGIN listservs. Data received were analyzed and incorporated into the appropriate feature classes where locality data were a superior representation of boundaries. Administrative Boundary geodatabase and shapefiles are unclipped to hydrography features by default. The clipped to hydro dataset is included as a separate shapefile download below.

Presented here are point clouds derived from aerial photography collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering approximately 13 km of the coastline near Big Sur, California. These point clouds are referenced to previously published lidar data and contain RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.7 through 2.0. Point clouds were clipped to an area of interest (AOI) using LASTools. The AOI was created in ArcGIS Pro 3.3.1.

Presented here are point clouds of approximately 13 km of the Big Sur coastline each showing erosion (as positive values) between two dates. The point cloud coordinates reflect topography at the later date. Change detection was computed using point clouds published in this data release and developed with structure-from-motion on aerial photography collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system. Ground points were identified in these point clouds using LAStools and manual reclassification of some protruding rocks as ground. Non-ground points were then removed, and the point clouds were clipped to an area of interest (AOI) of the cliff face using LASTools. We used the Multiscale Model-to-Model Cloud Comparison (M3C2) tool to calculate change in the cliff face between two flights. The M3C2 point cloud is a subsampled (at 0.25-m resolution) version of the point cloud of the second flight and M3C2 distances reflect erosion that occurred between the previous flight and this flight. This point cloud was then filtered using a combination of the Visible Atmospherically Resistant Index (VARI) and manual assessment to eliminate changes that do not reflect erosion, such as vegetation changes. It was then additionally filtered to include only points that the M3C2 algorithm considered significant (low uncertainty). Point cloud contains XYZ data and the following scalar fields: G-R/G+R-B (VARI), M3C2 distance, distance uncertainty, and significant change. Point cloud coordinates are in NAD83 UTM Zone 10 meters. The AOI was created in ArcGIS Pro 3.3.1. M3C2 distances, VARI calculation, and filtering were calculated in CloudCompare v2.12.4. Note that some flight dates represented in the topographic point cloud dataset will not have an associated M3C2 file because the product showed no erosion but substantial vegetation noise and was thus excluded.

The Virginia Geographic Information Network (VGIN) has coordinated the development and maintenance of a statewide Parcels data layer in conjunction with local governments across the Commonwealth. The Virginia Parcel dataset is aggregated as part of the VGIN Local Government Data Call update cycle. Localities are encouraged to submit data bi-annually and are included into the parcel dataset with their most recent geography.These tables are to be used in conjunction with theVirginia Parcel Datasetmost recent download to receive additional attribution beyond Map Number and Parcel ID. They are joined to the Parcel geography using the VGIN_QPID attribute on the Parcel feature class and local schema table. Attributes differ from locality to locality and what is provided in the geodatabase is a tabular representation of the geospatial attributes provided from the jurisdiction to VGIN with bi-annual data calls. Locality tables have the last received and processed date appended to the table name.If you have questions about attributes and usability of each schema set, please contact the jurisdiction"s GIS coordinator at theLocal GIS Contacts Page.GDB Version: ArcGIS Pro 3.3Additional Resources:Statewide Parcel Dataset

ArcGIS Pro data layers (version 3.3.2) of additional liquefaction hazard mapping presented in Javanbakht et al. (2025). Performance-Based Liquefaction Analysis and Probabilistic Liquefaction Hazard Mapping using CPT Data within the Fraser River delta, Canada. Soil Dynamics and Earthquake Engineering, 189, 109101, https://doi.org/10.1016/j.soildyn.2024.109101.

This data repository contains the data used for the study "Modelling Ecosystem Service Trends Under Contrasting Dutch Peatland and Agricultural Land-Use Futures ". In this dataset lies the input data, script/configuration files, output data, and processing/analysis data for the use of different InVEST ® models (https://naturalcapitalproject.stanford.edu/software/invest). Specifically, this data is based on four InVEST models used within the study: 1) Scenario Generator: Proximity-based; 2) Carbon Storage & Sequestration; 3) Nutrient Delivery Ratio; and 4) Seasonal Water Yield.Input data was obtained through a literature review and a search of available online data. InVEST models were run using InVEST 3.14.1 Workbench and ArcGIS Pro version 3.3.0. Resulting model outputs thereby cover scenario-based land-use land-cover maps, pool-dependent carbon storage and sequestration, nitrogen and phosphorus exports and loads, as well as precipitation, evapotranspiration, local recharge, baseflow, and quickflow raster data for the study area.

The Virginia Geographic Information Network (VGIN) has coordinated and manages the development of a consistent, seamless, statewide digital road centerline file with address, road name, and state route number attribution, as part of the Virginia Base Mapping Program (VBMP). The Road Centerline Program (RCL) leverages the Commonwealth"s investment in the VBMP digital orthophotography and is focused on creating a single statewide, consistent digital road file.The RCL data layer is a dynamic dataset supported and maintained by Virginia"s Local Governments, VDOT, and VGIN. VBMP RCL is extracted and provided back to local governments and state agencies in many geographic data sets every quarter.GDB Version: ArcGIS Pro 3.3Additional Resources:Routable RCL With Network Dataset GDB(ArcGIS Pro 3.2)Shapefile DownloadREST EndpointRoad Centerline Data StandardArcGIS LYR FileHistorical RCL & Ancillary Centerlines -Contact VGIN

¿Cuáles son los tramos de carretera más vulnerables en España?El mapa nos muestra información para representar los tramos de carretera de España más vulnerables en base a datos sobre la Intensidad Media Diaria (IMD) de cada tramo, la siniestralidad del municipio por el que transcurren los tramos y la distancia al hospital más cercano.El IMD se representa mediante líneas con diferentes colores y tamaños, morado y de mayor grosor para las más concurridas y amarillo y de menor grosor para las menos transitadas.La siniestralidad por municipios la podemos observar como círculos de diferentes tamaños, según el número total de accidentes en el año en cuestión. También se pintarán de color morado los que hayan sufrido más accidentes y de amarillo los que menos.Los tramos de carretera más vulnerables se han calculado de la siguiente forma: se han descartado aquellos que están a menos de 30 minutos de un hospital y, a su vez, que cruce o esté a menos de 10km de un municipio en el que haya habido al menos un accidente ese año. Los datos usados proceden del Living Atlas: Capa de Hospitales de España, capa de Siniestralidad por Municipios y la capa del Mapa de tráfico de la DGC 2021. El cálculo de los Buffer para hallar los tramos más vulnerables se ha realizado en ArcGIS Pro 3.3.1.