MultiEarth 2023 Deforestation Challenge -- Team FOREVER

• URL: http://arxiv.org/abs/2306.11762v1
• Date: Tue, 20 Jun 2023 09:10:06 GMT
• Title: MultiEarth 2023 Deforestation Challenge -- Team FOREVER
• Authors: Seunghan Park, Dongoo Lee, Yeonju Choi, SungTae Moon
• Abstract summary: It is important problem to accurately estimate deforestation of satellite imagery since this approach can analyse extensive area without direct human access. 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.
• Score: 0.2020917258669917
• License: http://creativecommons.org/licenses/by/4.0/
• 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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