Classification of Tropical Deforestation Drivers with Machine Learning and Satellite Image Time Series

Tropical deforestation is a major environmental problem with severe consequences such as carbon emissions or biodiversity loss. While much research focuses on monitoring and mapping deforestation, less attention is paid to understanding the various reasons and motivations behind it, known as deforestation drivers. Drivers can typically be identified from optical satellite imagery, but it is often necessary to view the deforested site at multiple points in time to determine the driver, making manual annotation of drivers laborious. In this work, we propose a deep learning model that classifies drivers from time series of Sentinel-2 images. The model combines convolutional, LSTM, and attention layers. To train the model, we use a large crowd-sourced dataset spanning across the tropics. We compare its results to other architectures and show that using time series can bring significant improvement in accuracy compared to single images, especially if a suitable architecture is used. Additionally, we analyze the attention scores produced by our model and show that it learns different strategies for different classes.