100+ datasets found
  1. a

    ArcGIS Pro Fundamentals

    • hub.arcgis.com
    Updated May 3, 2019
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    State of Delaware (2019). ArcGIS Pro Fundamentals [Dataset]. https://hub.arcgis.com/documents/ccd396a41cc944258e0d3c0461c473ea
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    Dataset updated
    May 3, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    Enroll in this plan to get familiar with the user interface, apply commonly used tools, and master the basics of mapping and analyzing data using ArcGIS Pro.Goals Install ArcGIS Pro and efficiently locate tools, options, and user interface elements. Add data to a map, symbolize map features to represent type, categories, or quantities; and optimize map display at various scales. Create a file geodatabase to organize and accurately maintain GIS data over time. Complete common mapping, editing, and analysis workflows.

  2. 11.2 ArcGIS Pro: Using Imagery

    • hub.arcgis.com
    • training-iowadot.opendata.arcgis.com
    Updated Mar 4, 2017
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    Iowa Department of Transportation (2017). 11.2 ArcGIS Pro: Using Imagery [Dataset]. https://hub.arcgis.com/documents/55d6890c874b44719bb3b34321bea385
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    Dataset updated
    Mar 4, 2017
    Dataset authored and provided by
    Iowa Department of Transportationhttps://iowadot.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Video based training seminar.

  3. U

    Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro

    • data.usgs.gov
    • catalog.data.gov
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    Sarah Black, Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro [Dataset]. http://doi.org/10.5066/P9RGW46K
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    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Authors
    Sarah Black
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Time period covered
    Dec 2, 2020
    Description

    GIS project files and imagery data required to complete the Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro tutorial. These data cover the area in and around Jezero crater, Mars.

  4. a

    Introducing Arcgis Pro

    • hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    Updated Dec 14, 2018
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    State of Delaware (2018). Introducing Arcgis Pro [Dataset]. https://hub.arcgis.com/documents/94e3d3c12bd341759ae7ee61602b3647
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    Dataset updated
    Dec 14, 2018
    Dataset authored and provided by
    State of Delaware
    Description

    ArcGIS Pro allows you to store multiple items, such as maps, layouts, tables, and charts, in a single project and work with them as needed. The application also responds contextually to your work. Tabs on the ribbon change depending on the type of item you're working with.In this tutorial, you'll explore the main components of the ArcGIS Pro user interface—the ribbon, views, and panes—and their interactions.

  5. 02.1 Integrating Data in ArcGIS Pro

    • hub.arcgis.com
    Updated Feb 16, 2017
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    Iowa Department of Transportation (2017). 02.1 Integrating Data in ArcGIS Pro [Dataset]. https://hub.arcgis.com/documents/cd5acdcc91324ea383262de3ecec17d0
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    Dataset updated
    Feb 16, 2017
    Dataset authored and provided by
    Iowa Department of Transportationhttps://iowadot.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

  6. f

    terraceDL: A geomorphology deep learning dataset of agricultural terraces in...

    • figshare.com
    bin
    Updated Mar 22, 2023
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    Aaron Maxwell (2023). terraceDL: A geomorphology deep learning dataset of agricultural terraces in Iowa, USA [Dataset]. http://doi.org/10.6084/m9.figshare.22320373.v2
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    binAvailable download formats
    Dataset updated
    Mar 22, 2023
    Dataset provided by
    figshare
    Authors
    Aaron Maxwell
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States, Iowa
    Description

    scripts.zip

    arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

    makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

    terraceDL.zip

    dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

  7. 13.4 Preparing to Perform Analysis Using ArcGIS Pro

    • hub.arcgis.com
    Updated Mar 4, 2017
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    Iowa Department of Transportation (2017). 13.4 Preparing to Perform Analysis Using ArcGIS Pro [Dataset]. https://hub.arcgis.com/documents/a2559c1da19b4b71880e7890d52c20cb
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    Dataset updated
    Mar 4, 2017
    Dataset authored and provided by
    Iowa Department of Transportationhttps://iowadot.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Your manager has just assigned you to help the Park Service select some new observation points within Dinosaur National Park. These new observation points should meet a set of criteria based on their location. Twenty potential observation points have been identified. So, what is your next step? How can you use ArcGIS Pro to accomplish the analysis efficiently and accurately?After completing this course, you will be able to perform the following tasks:Use the appropriate geoprocessing tool for a given spatial problem.Demonstrate multiple methods for accessing geoprocessing tools.Use ArcGIS Pro to set geoprocessing environments.

  8. a

    Operations Response - NAPSG Tutorial

    • napsg.hub.arcgis.com
    • prep-response-portal.napsgfoundation.org
    Updated Dec 5, 2019
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    NAPSG Foundation (2019). Operations Response - NAPSG Tutorial [Dataset]. https://napsg.hub.arcgis.com/documents/ae53a07c34f3453386eb59be3738434f
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    Dataset updated
    Dec 5, 2019
    Dataset authored and provided by
    NAPSG Foundation
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tutorial Audience: GIS / Technology SpecialistsEnd User Audience: Emergency Management Planning and Operations StaffProblem: Your County Emergency Management Agency is planning a training exercise and wants to make use of “Web GIS.” Typically, they have you print out a new wall map each operational period and the status of facilities (e.g. shelters) are maintained in spreadsheets. This time they want to coordinate planning and operations across multiple locations, with everyone having the most up to date information on a live map. For example, they want to be able update the status of evacuation zones and shelters without requiring GIS expertise. Can you provide them with a web app that gives them some simple tools and just the layers they need to get started? Use a simulated flood or any other incident type to guide you through this process.Solution: Operations Response AppRequirements: You will need a license for ArcGIS Pro and ArcGIS Online to complete this tutorial.Note: This application is used with the Public Information Application Tutorial.

  9. Microsoft Buildings Footprint Training Data with Heights

    • cityscapes-projects-gisanddata.hub.arcgis.com
    Updated Feb 27, 2019
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    Esri (2019). Microsoft Buildings Footprint Training Data with Heights [Dataset]. https://cityscapes-projects-gisanddata.hub.arcgis.com/datasets/esri::microsoft-buildings-footprint-training-data-with-heights-
    Explore at:
    Dataset updated
    Feb 27, 2019
    Dataset authored and provided by
    Esrihttp://esri.com/
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Area covered
    Description

    Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. As part of that project Microsoft shared 8 million digitized building footprints with height information used for training the Deep Learning Algorithm. This map layer includes all buildings with height information for the original training set that can be used in scene viewer and ArcGIS pro to create simple 3D representations of buildings. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.Click see Microsoft Building Layers in ArcGIS Online.Digitized building footprint by State and City

    Alabama Greater Phoenix City, Mobile, and Montgomery

    Arizona Tucson

    Arkansas Little Rock with 5 buildings just across the river from Memphis

    California Bakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to Cloverdale

    Colorado Interior of Denver

    Connecticut Enfield and Windsor Locks

    Delaware Dover

    Florida Tampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and Gainesville

    Georgia Columbus, Atlanta, and Augusta

    Illinois East St. Louis, downtown area, Springfield, Champaign and Urbana

    Indiana Indianapolis downtown and Jeffersonville downtown

    Iowa Des Moines

    Kansas Topeka

    Kentucky Louisville downtown, Covington and Newport

    Louisiana Shreveport, Baton Rouge and center of New Orleans

    Maine Augusta and Portland

    Maryland Baltimore

    Massachusetts Boston, South Attleboro, commercial area in Seekonk, and Springfield

    Michigan Downtown Detroit

    Minnesota Downtown Minneapolis

    Mississippi Biloxi and Gulfport

    Missouri Downtown St. Louis, Jefferson City and Springfield

    Nebraska Lincoln

    Nevada Carson City, Reno and Los Vegas

    New Hampshire Concord

    New Jersey Camden and downtown Jersey City

    New Mexico Albuquerque and Santa Fe

    New York Syracuse and Manhattan

    North Carolina Greensboro, Durham, and Raleigh

    North Dakota Bismarck

    Ohio Downtown Cleveland, downtown Cincinnati, and downtown Columbus

    Oklahoma Downtown Tulsa and downtown Oklahoma City

    Oregon Portland

    Pennsylvania Downtown Pittsburgh, Harrisburg, and Philadelphia

    Rhode Island The greater Providence area

    South Carolina Greensville, downtown Augsta, greater Columbia area and greater Charleston area

    South Dakota greater Pierre area

    Tennessee Memphis and Nashville

    Texas Lubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus Christi

    Utah Salt Lake City downtown

    Virginia Richmond

    Washington Greater Seattle area to Tacoma to the south and Marysville to the north

    Wisconsin Green Bay, downtown Milwaukee and Madison

    Wyoming Cheyenne

  10. f

    U-Net GIS Data

    • figshare.com
    zip
    Updated Apr 13, 2025
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    Shaun Williams (2025). U-Net GIS Data [Dataset]. http://doi.org/10.6084/m9.figshare.28755017.v3
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    zipAvailable download formats
    Dataset updated
    Apr 13, 2025
    Dataset provided by
    figshare
    Authors
    Shaun Williams
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The GIS database used in this project serves as a centralized repository for all spatial datasets required for wildfire threat analysis and model training. It includes CAL FIRE’s Wildland Fire Threat layer, which provides pixel-based classifications of wildfire potential across California, as well as transportation infrastructure layers, including primary and secondary roads and railways.To support impact analysis, 1,000-foot buffer zones were generated around each infrastructure feature to define zones of interest for wildfire segmentation. The database is structured for integration into both machine learning workflows and GIS environments, enabling seamless overlay, visualization, and spatial querying within platforms such as ArcGIS Pro or QGIS.

  11. a

    ArcGIS Pro Editing Essentials

    • hub.arcgis.com
    Updated Jan 17, 2019
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    State of Delaware (2019). ArcGIS Pro Editing Essentials [Dataset]. https://hub.arcgis.com/documents/delaware::arcgis-pro-editing-essentials/about
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    Dataset updated
    Jan 17, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    This seminar covers essential concepts to effectively manage your geospatial data using ArcGIS Pro. You will get familiar with the ArcGIS Pro editing environment, including the user interface and key options and settings that increase accuracy and efficiency while editing. The presenters highlight new capabilities in ArcGIS Pro that will streamline your editing workflows.

  12. B

    Toronto Land Use Spatial Data - parcel-level - (2019-2021)

    • borealisdata.ca
    Updated Feb 23, 2023
    + more versions
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    Marcel Fortin (2023). Toronto Land Use Spatial Data - parcel-level - (2019-2021) [Dataset]. http://doi.org/10.5683/SP3/1VMJAG
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 23, 2023
    Dataset provided by
    Borealis
    Authors
    Marcel Fortin
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Toronto
    Description

    Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

  13. a

    Create and Share ArcGIS Pro Tasks

    • hub.arcgis.com
    Updated May 3, 2019
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    State of Delaware (2019). Create and Share ArcGIS Pro Tasks [Dataset]. https://hub.arcgis.com/documents/delaware::create-and-share-arcgis-pro-tasks/about
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    Dataset updated
    May 3, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    Tasks are preconfigured steps that guide users through specific workflows. Learn the basic principles and options to design tasks and share them throughout your organization.Goals Create stand-alone tasks and task groups. Share tasks to be reused in multiple ArcGIS Pro projects.

  14. a

    Cartographic Creations in ArcGIS Pro

    • hub.arcgis.com
    • training-delaware.opendata.arcgis.com
    Updated May 3, 2019
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    State of Delaware (2019). Cartographic Creations in ArcGIS Pro [Dataset]. https://hub.arcgis.com/documents/19e7de7322d74e4b8761ba455b38c5cc
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    Dataset updated
    May 3, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    GoalsSymbolize dense point features.Add and label reference data.Configure a layout for print maps.

  15. Use Deep Learning to Assess Palm Tree Health

    • hub.arcgis.com
    Updated Mar 14, 2019
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    Esri Tutorials (2019). Use Deep Learning to Assess Palm Tree Health [Dataset]. https://hub.arcgis.com/documents/LearnGIS::use-deep-learning-to-assess-palm-tree-health/about
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    Dataset updated
    Mar 14, 2019
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Tutorials
    Description

    Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as the one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require lots of time and manpower. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the Deep Learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.

    To detect palm trees and calculate vegetation health, you only need ArcGIS Pro with the Image Analyst extension. To publish the palm tree health data as a feature service, you need ArcGIS Online and the Spatial Analyst extension.

    In this lesson you will build skills in these areas:

    • Creating training schema
    • Digitizing training samples
    • Using deep learning tools in ArcGIS Pro
    • Calculating VARI
    • Extracting data to points

    Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

  16. a

    Data from: Create a Project

    • hub.arcgis.com
    Updated Jan 17, 2019
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    State of Delaware (2019). Create a Project [Dataset]. https://hub.arcgis.com/documents/4f4c09e4004446b08826e39bd04eb418
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    Dataset updated
    Jan 17, 2019
    Dataset authored and provided by
    State of Delaware
    Description

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

  17. a

    CartoCards Tutorial #3

    • cartocards-centerforgis.hub.arcgis.com
    Updated Nov 4, 2021
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    University of Louisville Center for GIS (2021). CartoCards Tutorial #3 [Dataset]. https://cartocards-centerforgis.hub.arcgis.com/datasets/cartocards-tutorial-3-1
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    Dataset updated
    Nov 4, 2021
    Dataset authored and provided by
    University of Louisville Center for GIS
    Description

    Our second workshop introduced the ArcGIS Pro software environment as a platform for spatial data management and analysis. We focused on essential workflows for importing and geolocating spreadsheet data, querying and extracting a subset of existing feature data, data fusion through the Spatial Joins, and simple map layout design and creation.In this workshop we will build on the skills from Workshop #2, and introduce techniques for working with data possessing an explicit temporal component, or time-enabled data, in the language of ESRI.

  18. a

    Navigate Your Data

    • hub.arcgis.com
    Updated Jan 17, 2019
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    State of Delaware (2019). Navigate Your Data [Dataset]. https://hub.arcgis.com/documents/868b973d0e8c4ab0a04e67ccfa7138f2
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    Dataset updated
    Jan 17, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    An on-screen navigator can also be used to pan, zoom, rotate, and tilt the view.In this tutorial, you'll navigate a map and a scene using the Explore tool and the navigator. You'll also learn how to link views so your map and scene pan, zoom, and rotate together.Estimated time: 15 minutesSoftware requirements: ArcGIS Pro

  19. a

    Create Points from a Table

    • hub.arcgis.com
    Updated Jan 17, 2019
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    State of Delaware (2019). Create Points from a Table [Dataset]. https://hub.arcgis.com/documents/delaware::create-points-from-a-table/about
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    Dataset updated
    Jan 17, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    If you have geographic information stored as a table, ArcGIS Pro can display it on a map and convert it to spatial data. In this tutorial, you'll create spatial data from a table containing the latitude-longitude coordinates of huts in a New Zealand national park. Huts in New Zealand are equivalent to cabins in the United States—they may or may not have sleeping bunks, kitchen facilities, electricity, and running water. The table of hut locations is stored as a comma-separated values (CSV) file. CSV files are a common, nonproprietary file type for tabular data.Estimated time: 45 minutesSoftware requirements: ArcGIS Pro

  20. a

    Explore Your Data

    • hub.arcgis.com
    Updated Jan 17, 2019
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    State of Delaware (2019). Explore Your Data [Dataset]. https://hub.arcgis.com/documents/632e9c34d1ca4fd39c053102680ffd19
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    Dataset updated
    Jan 17, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    In this tutorial, you will explore several ways of working with attribute data. You'll get attribute values from pop-ups and panes. You'll format a table to show fields of interest. You'll query and summarize your data. Finally, you'll create a chart to represent attribute data visually.Estimated time: 45 minutesSoftware requirements: ArcGIS Pro

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State of Delaware (2019). ArcGIS Pro Fundamentals [Dataset]. https://hub.arcgis.com/documents/ccd396a41cc944258e0d3c0461c473ea

ArcGIS Pro Fundamentals

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2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
May 3, 2019
Dataset authored and provided by
State of Delaware
Description

Enroll in this plan to get familiar with the user interface, apply commonly used tools, and master the basics of mapping and analyzing data using ArcGIS Pro.Goals Install ArcGIS Pro and efficiently locate tools, options, and user interface elements. Add data to a map, symbolize map features to represent type, categories, or quantities; and optimize map display at various scales. Create a file geodatabase to organize and accurately maintain GIS data over time. Complete common mapping, editing, and analysis workflows.

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