100+ datasets found
  1. A

    Geospatial Deep Learning Seminar Online Course

    • data.amerigeoss.org
    html
    Updated Oct 18, 2024
    + more versions
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    AmericaView (2024). Geospatial Deep Learning Seminar Online Course [Dataset]. https://data.amerigeoss.org/dataset/geospatial-deep-learning-seminar-online-course
    Explore at:
    htmlAvailable download formats
    Dataset updated
    Oct 18, 2024
    Dataset provided by
    AmericaView
    License

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

    Description

    This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification.

    The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively.

    After completing this seminar you will be able to:

    1. explain how ANNs work including weights, bias, activation, and optimization.
    2. describe and explain different loss and assessment metrics and determine appropriate use cases.
    3. use the tensor data model to represent data as input for deep learning.
    4. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization.
    5. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data.
    6. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs.
    7. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.
    8. explain how object and instance segmentation are different from traditional CNNs and semantic segmentation and how they can be used to generate bounding boxes and feature masks for each instance of a class.
    9. use ArcGIS Pro to perform object detection (to obtain bounding boxes) and instance segmentation (to obtain pixel-level instance masks).
  2. Open-Source GIScience Online Course

    • ckan.americaview.org
    Updated Nov 2, 2021
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    ckan.americaview.org (2021). Open-Source GIScience Online Course [Dataset]. https://ckan.americaview.org/dataset/open-source-giscience-online-course
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    Dataset updated
    Nov 2, 2021
    Dataset provided by
    CKANhttps://ckan.org/
    License

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

    Description

    In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

  3. 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/d50cea3d161542b681333f1bc265029a
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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.

  4. a

    13.1 Spatial Analysis with ArcGIS Online

    • hub.arcgis.com
    • training-iowadot.opendata.arcgis.com
    Updated Mar 4, 2017
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    Iowa Department of Transportation (2017). 13.1 Spatial Analysis with ArcGIS Online [Dataset]. https://hub.arcgis.com/documents/IowaDOT::13-1-spatial-analysis-with-arcgis-online/about
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    Dataset updated
    Mar 4, 2017
    Dataset authored and provided by
    Iowa Department of Transportation
    License

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

    Description

    In this seminar, you will learn about the spatial analysis tools built directly into the ArcGIS.com map viewer. You will learn of the spatial analysis capabilities in ArcGIS Online for Organizations, whether for analyzing your own data, data that's publicly available on ArcGIS Online, or a combination of both. You will learn the overall features and benefits of ArcGIS Online Analysis, how to get started, and how to choose the right approach in order to solve a specific spatial problem.

  5. BOGS Training Metrics

    • catalog.data.gov
    • s.cnmilf.com
    • +1more
    Updated May 9, 2025
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    Bureau of Indian Affairs (BIA) (2025). BOGS Training Metrics [Dataset]. https://catalog.data.gov/dataset/bogs-training-metrics
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    Dataset updated
    May 9, 2025
    Dataset provided by
    Bureau of Indian Affairshttp://www.bia.gov/
    Description

    Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

  6. 10.4 Creating Web Applications Using Templates and Web AppBuilder for ArcGIS...

    • hub.arcgis.com
    • training-iowadot.opendata.arcgis.com
    Updated Mar 4, 2017
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    Iowa Department of Transportation (2017). 10.4 Creating Web Applications Using Templates and Web AppBuilder for ArcGIS [Dataset]. https://hub.arcgis.com/documents/317d8d6afba540448443b5630bae01be
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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

    This course demonstrates how to select, modify, create, and share web applications using ArcGIS Online. ArcGIS Online offers many different options for creating web applications that share web maps, web scenes, and spatial functions. But how do you decide which web application best meets your requirements? Each web application option implements different functions and showcases a specific look and feel. You can choose a web application that meets your organization's functional requirements, apply your organization's look and feel, and share your web map without writing any code.Two workflows will be introduced for creating web applications using ArcGIS Online:Applying your web map to an existing template applicationCreating your own web application using Web AppBuilder for ArcGISAfter completing this course, you will be able to do the following:Identify the components of a web application.Create a web application from an existing configurable app template.Create a web application using Web AppBuilder for ArcGIS.Use ArcGIS Online to deploy a web application.

  7. a

    03.6 Navigator for ArcGIS: An Introduction

    • hub.arcgis.com
    Updated Feb 18, 2017
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    Iowa Department of Transportation (2017). 03.6 Navigator for ArcGIS: An Introduction [Dataset]. https://hub.arcgis.com/documents/0f3eefd572a94e8594432c0f5d192d95
    Explore at:
    Dataset updated
    Feb 18, 2017
    Dataset authored and provided by
    Iowa Department of Transportation
    License

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

    Description

    Navigator for ArcGIS is a mobile app that gets your field workforce where it needs to be, unlocking efficiency gains and improving reliability. Learn how it works offline in seamless interaction with ArcGIS field apps. Experience how to use the data provided, your own custom data, or both to search and navigate directly to your organization's assets.This seminar was developed to support ArcGIS Online and Navigator for ArcGIS.

  8. ArcGIS for Schools

    • lecturewithgis.co.uk
    Updated Jan 17, 2023
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    Esri UK Education (2023). ArcGIS for Schools [Dataset]. https://lecturewithgis.co.uk/datasets/arcgis-for-schools
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    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri UK Education
    Description

    Esri UK is providing a digital mapping platform and expertise in biodiversity mapping for the National Education Nature Park. We are providing the Department of Education with ArcGIS Online - an extensible web-based mapping platform to provide staff and students with geospatial tools that will allow them to view, capture, store, analyse and monitor environmental and biodiversity data. We are also providing Professional Services to be delivered using an agile methodology, along with training to key stakeholders.To deploy geospatial tools to all schools, we are using the existing ArcGIS for Schools program.

  9. d

    Golf Courses

    • catalog.data.gov
    • data.seattle.gov
    • +2more
    Updated Aug 23, 2025
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    City of Seattle ArcGIS Online (2025). Golf Courses [Dataset]. https://catalog.data.gov/dataset/golf-courses-6a22b
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    Dataset updated
    Aug 23, 2025
    Dataset provided by
    City of Seattle ArcGIS Online
    Description

    Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

  10. n

    Operations Response - NAPSG Tutorial

    • prep-response-portal.napsgfoundation.org
    • napsg.hub.arcgis.com
    Updated Dec 5, 2019
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    NAPSG Foundation (2019). Operations Response - NAPSG Tutorial [Dataset]. https://prep-response-portal.napsgfoundation.org/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.

  11. Early Education and Training Centre in Hong Kong

    • opendata.esrichina.hk
    • data-esrihk.opendata.arcgis.com
    • +1more
    Updated Jul 21, 2021
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    Esri China (Hong Kong) Ltd. (2021). Early Education and Training Centre in Hong Kong [Dataset]. https://opendata.esrichina.hk/maps/38382ca887e84874adef6722b8498dcf
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    Dataset updated
    Jul 21, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri China (Hong Kong) Ltd.
    Area covered
    Description

    This web map shows the location of early education and training centres in Hong Kong. It is a set of data made available by the Social Welfare Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

  12. a

    Add Data to Your Project

    • hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    Updated Jan 17, 2019
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    State of Delaware (2019). Add Data to Your Project [Dataset]. https://hub.arcgis.com/documents/1424171168284efab99d7b8727a85fa0
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    Dataset updated
    Jan 17, 2019
    Dataset authored and provided by
    State of Delaware
    Description

    You can also add data from your ArcGIS Online or ArcGIS Enterprise portal, or another portal such as the Living Atlas of the World. In this tutorial, you'll add data from Living Atlas, from your project geodatabase, and from a folder connection.Estimated time: 30 minutesSoftware requirements: ArcGIS ProArcGIS Online or ArcGIS Enterprise portal connection

  13. d

    Light Gray Canvas

    • catalog.data.gov
    • data.baltimorecity.gov
    • +17more
    Updated Sep 11, 2021
    + more versions
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    esri_en (2021). Light Gray Canvas [Dataset]. https://catalog.data.gov/ne/dataset/light-gray-canvas
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    Dataset updated
    Sep 11, 2021
    Dataset provided by
    esri_en
    Description

    This map is designed to focus attention on your thematic content by providing a neutral background with minimal colors, labels, and features. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the Light Gray Base and Light Gray Reference.

  14. Oceans

    • opendata-cosagis.opendata.arcgis.com
    • africageoportal.com
    • +14more
    Updated Mar 20, 2019
    + more versions
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    esri_en (2019). Oceans [Dataset]. https://opendata-cosagis.opendata.arcgis.com/maps/620875bf8a1945e799cf8bd5f25784d7
    Explore at:
    Dataset updated
    Mar 20, 2019
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    esri_en
    Area covered
    Description

    This map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the Ocean Basemap.

  15. a

    03.8 Navigator for ArcGIS: Creating Custom Navigation Maps

    • training-iowadot.opendata.arcgis.com
    Updated Feb 18, 2017
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    Iowa Department of Transportation (2017). 03.8 Navigator for ArcGIS: Creating Custom Navigation Maps [Dataset]. https://training-iowadot.opendata.arcgis.com/documents/a5c498dac1af42fd931620964c75d0e9
    Explore at:
    Dataset updated
    Feb 18, 2017
    Dataset authored and provided by
    Iowa Department of Transportation
    License

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

    Description

    Navigator for ArcGIS is a mobile app that gets your field workforce where it needs to be, unlocking efficiency gains and improving reliability. Learn how it works offline in seamless interaction with ArcGIS field apps. Experience how to use the data provided, your own custom data, or both to search and navigate directly to your organization's assets.This seminar was developed to support ArcGIS Online and Navigator for ArcGIS.

  16. 03.4 Modernize Your Field Workflows Using Collector for ArcGIS

    • hub.arcgis.com
    • training-iowadot.opendata.arcgis.com
    Updated Feb 18, 2017
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    Iowa Department of Transportation (2017). 03.4 Modernize Your Field Workflows Using Collector for ArcGIS [Dataset]. https://hub.arcgis.com/documents/eaa289cad0ad48d5aa4709284739e60a
    Explore at:
    Dataset updated
    Feb 18, 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

    In this seminar, you will learn how to equip field workers with easy-to-use maps that run on a smartphone or tablet using Collector for ArcGIS, an app included with an ArcGIS Online organizational subscriptions or Portal for ArcGIS. You will see how the maps are used to collect accurate data in the field-even when access to a WiFi connection or cellular service is not available-and quickly share data updates with the organization when connected. You will learn how to help your organization reduce errors, increase productivity, and improve data quality by replacing paper-based workflows with maps that feature data-driven, intelligent forms.This seminar was developed to support the following:ArcGIS OnlineArcGIS Online Organizational AccountUser role or equivalentCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

  17. Getting to Know Web GIS, fourth edition

    • dados-edu-pt.hub.arcgis.com
    Updated Aug 13, 2020
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    Esri Portugal - Educação (2020). Getting to Know Web GIS, fourth edition [Dataset]. https://dados-edu-pt.hub.arcgis.com/datasets/getting-to-know-web-gis-fourth-edition
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    Dataset updated
    Aug 13, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Portugal - Educação
    License

    Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
    License information was derived automatically

    Description

    Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

  18. Bezirke - Berlin

    • portal-esri-de.opendata.arcgis.com
    • gis-team-qualitas-esri-training.opendata.arcgis.com
    • +3more
    Updated Jul 12, 2018
    + more versions
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    Esri Deutschland (2018). Bezirke - Berlin [Dataset]. https://portal-esri-de.opendata.arcgis.com/datasets/esri-de-content::bezirke-berlin
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    Dataset updated
    Jul 12, 2018
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Deutschland
    Area covered
    Description

    Daten des amtlichen Liegenschaftskatsterinformationssystems (ALKIS) - Die Bezirksgrenzen der 12 Berliner Bezirke.Quelle: Geoportal BerlinVerarbeitungsprozesse: WFS "ALKIS Bezirke" wurde in ArcGIS Pro importiert, nach Web Mercator projiziert und als Web Layer in ArcGIS Online veröffentlicht.

  19. 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

  20. 10.0 Get Started with Configurable Apps

    • hub.arcgis.com
    • training-iowadot.opendata.arcgis.com
    Updated Mar 3, 2017
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    Iowa Department of Transportation (2017). 10.0 Get Started with Configurable Apps [Dataset]. https://hub.arcgis.com/documents/4eb0d88c2a3d47c2bd01d7c2c44c3a2c
    Explore at:
    Dataset updated
    Mar 3, 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

    In this seminar, the presenters introduce some of the most popular configurable apps and guide you through the steps to share a web map as a configurable app and then modify app elements to reflect your brand, purpose, and audience. You will see how configurable apps can complete the user experience of your map as well as how to embed your finished app in a website or another app, such as a story map.This seminar was developed to support the following:ArcGIS Online

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AmericaView (2024). Geospatial Deep Learning Seminar Online Course [Dataset]. https://data.amerigeoss.org/dataset/geospatial-deep-learning-seminar-online-course

Geospatial Deep Learning Seminar Online Course

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Dataset updated
Oct 18, 2024
Dataset provided by
AmericaView
License

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

Description

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification.

The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively.

After completing this seminar you will be able to:

  1. explain how ANNs work including weights, bias, activation, and optimization.
  2. describe and explain different loss and assessment metrics and determine appropriate use cases.
  3. use the tensor data model to represent data as input for deep learning.
  4. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization.
  5. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data.
  6. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs.
  7. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.
  8. explain how object and instance segmentation are different from traditional CNNs and semantic segmentation and how they can be used to generate bounding boxes and feature masks for each instance of a class.
  9. use ArcGIS Pro to perform object detection (to obtain bounding boxes) and instance segmentation (to obtain pixel-level instance masks).
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