Monitoring Forest Changes With Foundation Models and Sentinel-2 Time Series

Abstract

Monitoring forest areas is of paramount importance to maintain environmental sustainability. The scalability of forest monitoring solutions is effectively offered by satellite imaging, where images of various modalities are acquired in orbit and cover large areas. However, building machine learning models for such downstream Earth observation (EO) tasks is challenging due to the limited amounts of ground-truth datasets. We tackle this issue and introduce an end-to-end deep learning pipeline to detect forest changes from Sentinel-2 time series of multispectral images (MSIs). It benefits from a foundation model (FM) fine-tuned over a small yet spatially diverse dataset. The experiments showed that not only does it outperform other deep models but also it requires minimal user intervention before the fine-tuning process.