A new automated remote sensing segmentation pipeline, EcoMapper, plus an empirical index relating achievable segmentation quality to feature size and image resolution.
MultiEarth 2023 Deforestation Challenge -- Team FOREVER
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abstract
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.
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Segmentation of arbitrary features in very high resolution remote sensing imagery
A new automated remote sensing segmentation pipeline, EcoMapper, plus an empirical index relating achievable segmentation quality to feature size and image resolution.