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\begin{document}

\title{Deforestation Area Segmentation in Satellite Image from Multimodal
Remote Sensing Data}



\author[1]{Dongoo Lee}%
\author[1]{Yeonju Choi}%
\author[2]{SungTae Moon}%
\affil[1]{Korea Aerospace Research Institute}%
\affil[2]{Chungbuk National University}%


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  \date{May 27, 2024}


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\begin{abstract}
Deforestation of the Amazon rainforest is approaching the worst in
history. To protect against deforestation, it is necessary to accurately
estimate the deforestation area. However, it is difficult to analyze
large areas without direct human access. In addition, even if
deforestation is estimated using satellite images, the presence of
extensive cloud cover during the rainy season makes it challenging to
obtain a clear view of the ground surface. In this paper, we propose a
segmentation method based on deep learning and post-processing to
predict the deforestation status in the Amazon rainforest area. To train
and predict the deforestation area, we utilize a multi-modal satellite
imagery dataset, including Sentinel-1, Sentinel-2, and Landsat 8. The
proposed approach achieves the highest performance in the official CVPR
MultiEarth Workshop 2023 challenge.%
\end{abstract}%



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