Deep Transformer-Based Network Deforestation Detection in the Brazilian Amazon Using Sentinel-2 Imagery
Mariam Alshehri, Anes Ouadou, Grant J. Scott
Abstract
Deforestation poses a critical environmental challenge with far-reaching impacts on climate change, biodiversity, and local communities. As such, detecting and monitoring deforestation are crucial, and recent advancements in deep learning and remote sensing technologies offer a promising solution to this challenge. In this study, we adapt ChangeFormer, a transformer-based framework, to detect deforestation in the Brazilian Amazon, employing the attention mechanism to analyze spatial and temporal patterns in bi-temporal satellite images. To assess the model’s effectiveness, we employed a robust approach to create a deforestation detection dataset, utilizing Sentinel-2 imagery from select conservation areas in the Brazilian Amazon throughout 2020 and 2021. Our dataset comprises 7,734 pairs of bi-temporal image chips with a resolution of 256×256 pixels and 1,406 pairs of image chips with a resolution of 512×512 pixels. The model achieved an overall accuracy of 93% with corresponding F1 score of 90% and IoU score of 82%. These results demonstrate the potential of transformer-based networks for accurate and efficient deforestation detection.