Deforestation Detection in the Brazilian Amazon Using Transformer-based Networks

Abstract

Deforestation is a critical environmental issue that has far-reaching impacts on climate change, biodiversity, and the livelihoods of local communities. Conventional methods such as field surveys and map interpretation are not feasible, especially in vast regions like the Brazilian Amazon. In this paper, we adapt ChangeFormer, a transformer-based change detection model, to detect deforestation in the Brazilian Amazon, leveraging the attention mechanism to capture spatial and temporal dependencies in bi-temporal satellite images. To evaluate the model’s performance, we implemented a rigorous methodology to create a deforestation detection dataset using Sentinel-2 images of selected conservation units in the Brazilian Amazon during 2020 and 2021. The model achieved a high accuracy of 94%, demonstrating the potential of transformer-based networks for accurate and efficient deforestation detection.