Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software
Deep learning classification of invasive species using widely-used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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The University of Florida George A. Smathers Libraries
2021-10-01
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| Series: | EDIS |
| Online Access: | https://journals.flvc.org/edis/article/view/127433 |
| _version_ | 1797196885411233792 |
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| author | Amr Abd-Elrahman Katie Britt Tao Liu |
| author_facet | Amr Abd-Elrahman Katie Britt Tao Liu |
| author_sort | Amr Abd-Elrahman |
| collection | DOAJ |
| description |
Deep learning classification of invasive species using widely-used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more widely accessible to GIS analysts, researchers, and graduate students working with remotely sensed data in the natural resource field.
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| first_indexed | 2024-04-24T06:35:11Z |
| format | Article |
| id | doaj.art-0cef551530474f50b589fed69fd80f8d |
| institution | Directory Open Access Journal |
| issn | 2576-0009 |
| language | English |
| last_indexed | 2024-04-24T06:35:11Z |
| publishDate | 2021-10-01 |
| publisher | The University of Florida George A. Smathers Libraries |
| record_format | Article |
| series | EDIS |
| spelling | doaj.art-0cef551530474f50b589fed69fd80f8d2024-04-23T04:30:22ZengThe University of Florida George A. Smathers LibrariesEDIS2576-00092021-10-0120215Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro SoftwareAmr Abd-Elrahman0Katie Britt1Tao Liu2University of FloridaUniversity of FloridaMichigan Technological University Deep learning classification of invasive species using widely-used ArcGIS Pro software and increasingly common drone imagery can aid in identification and management of natural areas. A step-by-step implementation, with associated data for users to access, is presented to make this technology more widely accessible to GIS analysts, researchers, and graduate students working with remotely sensed data in the natural resource field. https://journals.flvc.org/edis/article/view/127433 |
| spellingShingle | Amr Abd-Elrahman Katie Britt Tao Liu Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software EDIS |
| title | Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software |
| title_full | Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software |
| title_fullStr | Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software |
| title_full_unstemmed | Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software |
| title_short | Deep Learning Classification of High-Resolution Drone Images Using the ArcGIS Pro Software |
| title_sort | deep learning classification of high resolution drone images using the arcgis pro software |
| url | https://journals.flvc.org/edis/article/view/127433 |
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