has been cited by the following article(s):
[1]
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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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[2]
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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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[3]
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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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[4]
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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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[5]
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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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[6]
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Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning
Remote Sensing,
2021
DOI:10.3390/rs13081509
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[7]
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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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