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arXiv · 2206.10831

MultiEarth 2022 Deforestation Challenge -- ForestGump

Dongoo Lee·Yeonju Choi
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Abstract

The estimation of deforestation in the Amazon Forest is challenge task because of the vast size of the area and the difficulty of direct human access. However, it is a crucial problem in that deforestation results in serious environmental problems such as global climate change, reduced biodiversity, etc. In order to effectively solve the problems, satellite imagery would be a good alternative to estimate the deforestation of the Amazon. With a combination of optical images and Synthetic aperture radar (SAR) images, observation of such a massive area regardless of weather conditions become possible. In this paper, we present an accurate deforestation estimation method with conventional UNet and comprehensive data processing. The diverse channels of Sentinel-1, Sentinel-2 and Landsat 8 are carefully selected and utilized to train deep neural networks. With the proposed method, deforestation status for novel queries are successfully estimated with high accuracy.

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Dongoo Lee, Yeonju Choi. 2022-06-22. MultiEarth 2022 Deforestation Challenge -- ForestGump. https://arxiv.org/abs/2206.10831

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