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Deforestation Area Segmentation in Satellite Image from Multimodal Remote Sensing Data
  • Dongoo Lee,
  • Yeonju Choi,
  • SungTae Moon
Dongoo Lee
Korea Aerospace Research Institute
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Yeonju Choi
Korea Aerospace Research Institute
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SungTae Moon
Chungbuk National University

Corresponding Author:[email protected]

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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.
25 Oct 2023Submitted to Electronics Letters
26 Oct 2023Assigned to Editor
26 Oct 2023Submission Checks Completed
03 Nov 2023Reviewer(s) Assigned