MultiEarth 2023 Deforestation Challenge -- Team FOREVER
This addresses deforestation monitoring for environmental researchers and policymakers, though it appears incremental as it applies existing frameworks to a specific domain challenge.
The paper tackles deforestation estimation in the Amazon rainforest by developing a multi-view learning strategy using multi-modal satellite imagery (Sentinel-1, Sentinel-2, Landsat 8) with deep neural networks, achieving effective and accurate predictions for new queries.
It is important problem to accurately estimate deforestation of satellite imagery since this approach can analyse extensive area without direct human access. However, it is not simple problem because of difficulty in observing the clear ground surface due to extensive cloud cover during long rainy season. In this paper, we present a multi-view learning strategy to predict deforestation status in the Amazon rainforest area with latest deep neural network models. Multi-modal dataset consists of three types of different satellites imagery, Sentinel-1, Sentinel-2 and Landsat 8 is utilized to train and predict deforestation status. MMsegmentation framework is selected to apply comprehensive data augmentation and diverse networks. The proposed method effectively and accurately predicts the deforestation status of new queries.