MultiEarth 2022 Deforestation Challenge -- ForestGump
This addresses the challenge of monitoring deforestation in remote areas like the Amazon, which is crucial for mitigating environmental impacts such as climate change and biodiversity loss, but the approach is incremental.
The paper tackled the problem of estimating deforestation in the Amazon Forest by using satellite imagery, achieving high accuracy in estimating deforestation status for novel queries.
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.