Enhancing Deforestation Detection through Multi-Domain Adaptation with Uncertainty Estimation

Deep learning models have shown great potential in scientific research, particularly in remote sensing, for monitoring natural resources, environmental changes, land cover and land use. Semantic segmentation techniques enable land cover classification, change detection, object identification, and vegetation health assessment, among other applications. However, their effectiveness relies on large labeled datasets, which are costly and time-consuming to obtain. Domain adaptation (DA) techniques address this challenge by transferring knowledge from a labeled source domain to one or more unlabeled target domains. While most DA research focuses on a single target domain, multi-target and multi-source scenarios remain underexplored. This work proposes a deep learning approach that uses Domain Adversarial Neural Networks (DANN) for deforestation detection in multi-domain settings. Additionally, an uncertainty estimation phase is introduced to guide human review in high-uncertainty areas. Our approach is evaluated on Landsat-8 images from the Amazon and Brazilian Cerrado biomes. In multi-target experiments, a single source domain contains labeled data, while samples from the target domains are unlabeled. In multi-source scenarios, labeled samples from multiple source domains are used to train the deep leraning models, later evaluated on a single target domain. The results show significant accuracy improvements over lower-bound baselines, as indicated by F1-score and mean Average Precision (mAP) values. Furthermore, uncertainty-based review showed a further potential to enhance performance, reaching upper-bound baselines in certain domain combinations.