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A novel deep learning approach based on automatic weighted average ensemble for accurate forest burn scar extraction in Indian tropical deciduous forest
Earth Science Informatics,
2025
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FLOGA: A Machine-Learning-Ready Dataset, a Benchmark, and a Novel Deep Learning Model for Burnt Area Mapping With Sentinel-2
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
2024
DOI:10.1109/JSTARS.2024.3381737
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[3]
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Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques
Scientific Reports,
2024
DOI:10.1038/s41598-024-60512-w
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FLOGA: A Machine-Learning-Ready Dataset, a Benchmark, and a Novel Deep Learning Model for Burnt Area Mapping With Sentinel-2
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
2024
DOI:10.1109/JSTARS.2024.3381737
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Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques
Scientific Reports,
2024
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SSU-Net: A Model for Mapping Burned Areas Using Landsat-8 and Sentinel-2
IEEE Transactions on Geoscience and Remote Sensing,
2024
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Mapping recent wildfires in Greece and the associated built-up losses
Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX,
2024
DOI:10.1117/12.3031800
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[8]
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Identifying Forest Burned Area Using a Deep Learning Model Based on Post-Fire Optical and SAR Remote Sensing Images
IEEE Access,
2024
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Evaluating the potential of burn severity mapping and transferability of Copernicus EMS data using Sentinel-2 imagery and machine learning approaches
GIScience & Remote Sensing,
2023
DOI:10.1080/15481603.2023.2192157
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[10]
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Evaluating the potential of burn severity mapping and transferability of Copernicus EMS data using Sentinel-2 imagery and machine learning approaches
GIScience & Remote Sensing,
2023
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[11]
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Deep learning-based burned forest areas mapping via Sentinel-2 imagery: a comparative study
Environmental Science and Pollution Research,
2023
DOI:10.1007/s11356-023-31575-5
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[12]
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Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems
Remote Sensing,
2022
DOI:10.3390/rs14030602
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[13]
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Machine Learning-Based Forest Burned Area Detection with Various Input Variables: A Case Study of South Korea
Applied Sciences,
2022
DOI:10.3390/app121910077
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Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning
Remote Sensing,
2021
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A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing
Remote Sensing,
2021
DOI:10.3390/rs13112214
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