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.