Application of Artificial Intelligence in Land Use and Land Cover Mapping II
Advances in Earth observation and high-performance computing are revolutionizing how we monitor land cover and support sustainable development. This volume explores cutting-edge methods in land cover classification, highlighting deep learning applications such as semantic segmentation, object detect...
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| Format: | Online |
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| Language: | English |
| Published: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Online Access: | ONIX_20250812T110751_9783725839926_280 |
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| _version_ | 1869518485226258432 |
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| collection | Directory of Open Access Books |
| description | Advances in Earth observation and high-performance computing are revolutionizing how we monitor land cover and support sustainable development. This volume explores cutting-edge methods in land cover classification, highlighting deep learning applications such as semantic segmentation, object detection, and temporal analysis. Key contributions include the ABNet model for enhanced feature representation, accuracy assessments of 30-meter land cover products, CNN-based wildfire mapping, and the segmentation of China’s coastal wetlands. These studies showcase AI's growing role in environmental monitoring and promote innovative and interdisciplinary solutions for managing landscape changes. |
| format | Online |
| id | doab-20.500.12854ir-165525 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1655252025-08-12T09:52:00Z Application of Artificial Intelligence in Land Use and Land Cover Mapping II Abbas, Sawaid Nichol, Janet E. Qamer, Faisal M. Xu, Jianchu deep learning remote sensing land use classification sentinel time series synthetic aperture radar (SAR) crop mapping time-series images constrained clustering active constraint learning Google Earth Engine (GEE) LULC transitions vegetation dynamics CFLR model land use policies Billion Tree Tsunami Project (BTTP) Ravi Urban Development Plan (RUDP) Master Plan 2050 building extraction high-resolution remote sensing image weakly supervised semantic segmentation self-attentive aggregation class activation map double branch CNN semantic segmentation buildings and waters Honghe Hani Rice Terraces Landsat land use/land cover phenology Google Earth Engine SegFormer coastal wetland remote sensing images machine learning wildfire assessment random forest fire occurrence land cover validation dataset accuracy assessment consistency analysis stratified random sampling backbone network landcover classification aggregated feature thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Advances in Earth observation and high-performance computing are revolutionizing how we monitor land cover and support sustainable development. This volume explores cutting-edge methods in land cover classification, highlighting deep learning applications such as semantic segmentation, object detection, and temporal analysis. Key contributions include the ABNet model for enhanced feature representation, accuracy assessments of 30-meter land cover products, CNN-based wildfire mapping, and the segmentation of China’s coastal wetlands. These studies showcase AI's growing role in environmental monitoring and promote innovative and interdisciplinary solutions for managing landscape changes. 2025-08-12T09:51:58Z 2025-08-12T09:51:58Z 2025 book ONIX_20250812T110751_9783725839926_280 9783725839926 9783725839919 https://directory.doabooks.org/handle/20.500.12854/165525 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10819 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3991-9 10.3390/books978-3-7258-3991-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725839926 9783725839919 234 open access |
| spellingShingle | deep learning remote sensing land use classification sentinel time series synthetic aperture radar (SAR) crop mapping time-series images constrained clustering active constraint learning Google Earth Engine (GEE) LULC transitions vegetation dynamics CFLR model land use policies Billion Tree Tsunami Project (BTTP) Ravi Urban Development Plan (RUDP) Master Plan 2050 building extraction high-resolution remote sensing image weakly supervised semantic segmentation self-attentive aggregation class activation map double branch CNN semantic segmentation buildings and waters Honghe Hani Rice Terraces Landsat land use/land cover phenology Google Earth Engine SegFormer coastal wetland remote sensing images machine learning wildfire assessment random forest fire occurrence land cover validation dataset accuracy assessment consistency analysis stratified random sampling backbone network landcover classification aggregated feature thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general Application of Artificial Intelligence in Land Use and Land Cover Mapping II |
| title | Application of Artificial Intelligence in Land Use and Land Cover Mapping II |
| title_full | Application of Artificial Intelligence in Land Use and Land Cover Mapping II |
| title_fullStr | Application of Artificial Intelligence in Land Use and Land Cover Mapping II |
| title_full_unstemmed | Application of Artificial Intelligence in Land Use and Land Cover Mapping II |
| title_short | Application of Artificial Intelligence in Land Use and Land Cover Mapping II |
| title_sort | application of artificial intelligence in land use and land cover mapping ii |
| topic | deep learning remote sensing land use classification sentinel time series synthetic aperture radar (SAR) crop mapping time-series images constrained clustering active constraint learning Google Earth Engine (GEE) LULC transitions vegetation dynamics CFLR model land use policies Billion Tree Tsunami Project (BTTP) Ravi Urban Development Plan (RUDP) Master Plan 2050 building extraction high-resolution remote sensing image weakly supervised semantic segmentation self-attentive aggregation class activation map double branch CNN semantic segmentation buildings and waters Honghe Hani Rice Terraces Landsat land use/land cover phenology Google Earth Engine SegFormer coastal wetland remote sensing images machine learning wildfire assessment random forest fire occurrence land cover validation dataset accuracy assessment consistency analysis stratified random sampling backbone network landcover classification aggregated feature thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| topic_facet | deep learning remote sensing land use classification sentinel time series synthetic aperture radar (SAR) crop mapping time-series images constrained clustering active constraint learning Google Earth Engine (GEE) LULC transitions vegetation dynamics CFLR model land use policies Billion Tree Tsunami Project (BTTP) Ravi Urban Development Plan (RUDP) Master Plan 2050 building extraction high-resolution remote sensing image weakly supervised semantic segmentation self-attentive aggregation class activation map double branch CNN semantic segmentation buildings and waters Honghe Hani Rice Terraces Landsat land use/land cover phenology Google Earth Engine SegFormer coastal wetland remote sensing images machine learning wildfire assessment random forest fire occurrence land cover validation dataset accuracy assessment consistency analysis stratified random sampling backbone network landcover classification aggregated feature thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general |
| url | ONIX_20250812T110751_9783725839926_280 |