Deep Learning and Transformers’ Methods Applied to Remotely Captured Data
The areas of machine learning and deep learning have experienced impressive progress in recent years. This progress has mainly been driven by the availability of high processing performance at an affordable cost and a large quantity of data. Most state-of-the-art techniques today are based on deep n...
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| Médium: | Online |
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| Jazyk: | angličtina |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Témata: | |
| On-line přístup: | ONIX_20240704_9783725805853_60 |
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| _version_ | 1869521966036156416 |
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| collection | Directory of Open Access Books |
| description | The areas of machine learning and deep learning have experienced impressive progress in recent years. This progress has mainly been driven by the availability of high processing performance at an affordable cost and a large quantity of data. Most state-of-the-art techniques today are based on deep neural networks or the more recently proposed transformers. This progress has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently. Among the various research areas that have been significantly impacted by this progress is the processing of remotely captured data such as airborne and spaceborne passive and active imagery, underwater imagery, mobile mapping data, etc. This collection gathered cutting-edge contributions from researchers using deep learning and transformers for remote sensing and for processing remotely captured data. |
| format | Online |
| id | doab-20.500.12854ir-139264 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1392642024-07-04T09:33:59Z Deep Learning and Transformers’ Methods Applied to Remotely Captured Data Akhloufi, Moulay A. Shahbazi, Mozhdeh Remote Sensing Super-Resolution Deep Learning Ship Detection Satellite Imaging Transformer Models Image Classification Terrain Analysis LiDAR Data Point Cloud Transformer thema EDItEUR::R Earth Sciences, Geography, Environment, Planning thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography::RGW Geographical information systems, geodata and remote sensing The areas of machine learning and deep learning have experienced impressive progress in recent years. This progress has mainly been driven by the availability of high processing performance at an affordable cost and a large quantity of data. Most state-of-the-art techniques today are based on deep neural networks or the more recently proposed transformers. This progress has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently. Among the various research areas that have been significantly impacted by this progress is the processing of remotely captured data such as airborne and spaceborne passive and active imagery, underwater imagery, mobile mapping data, etc. This collection gathered cutting-edge contributions from researchers using deep learning and transformers for remote sensing and for processing remotely captured data. 2024-07-04T09:33:56Z 2024-07-04T09:33:56Z 2024 book ONIX_20240704_9783725805853_60 9783725805853 9783725805860 https://directory.doabooks.org/handle/20.500.12854/139264 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/topic/9260 https://mdpi.com/books/pdfview/topic/9260 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0586-0 10.3390/books978-3-7258-0586-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725805853 9783725805860 348 open access |
| spellingShingle | Remote Sensing Super-Resolution Deep Learning Ship Detection Satellite Imaging Transformer Models Image Classification Terrain Analysis LiDAR Data Point Cloud Transformer thema EDItEUR::R Earth Sciences, Geography, Environment, Planning thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography::RGW Geographical information systems, geodata and remote sensing Deep Learning and Transformers’ Methods Applied to Remotely Captured Data |
| title | Deep Learning and Transformers’ Methods Applied to Remotely Captured Data |
| title_full | Deep Learning and Transformers’ Methods Applied to Remotely Captured Data |
| title_fullStr | Deep Learning and Transformers’ Methods Applied to Remotely Captured Data |
| title_full_unstemmed | Deep Learning and Transformers’ Methods Applied to Remotely Captured Data |
| title_short | Deep Learning and Transformers’ Methods Applied to Remotely Captured Data |
| title_sort | deep learning and transformers methods applied to remotely captured data |
| topic | Remote Sensing Super-Resolution Deep Learning Ship Detection Satellite Imaging Transformer Models Image Classification Terrain Analysis LiDAR Data Point Cloud Transformer thema EDItEUR::R Earth Sciences, Geography, Environment, Planning thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography::RGW Geographical information systems, geodata and remote sensing |
| topic_facet | Remote Sensing Super-Resolution Deep Learning Ship Detection Satellite Imaging Transformer Models Image Classification Terrain Analysis LiDAR Data Point Cloud Transformer thema EDItEUR::R Earth Sciences, Geography, Environment, Planning thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography::RGW Geographical information systems, geodata and remote sensing |
| url | ONIX_20240704_9783725805853_60 |