Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring
This work addresses domain adaptation challenges in Earth observation for forest monitoring, but it is incremental as it builds on existing assumptions and methods.
The paper tackles the problem of cross-domain regression in remote sensing for forest monitoring by introducing a new dataset with aerial and satellite imagery across five countries and three tasks, and proposes manifold diffusion for regression as a baseline, showing comparative advantages of inductive and transductive methods in low-data regimes.
Image-level regression is an important task in Earth observation, where visual domain and label shifts are a core challenge hampering generalization. However, cross-domain regression within remote sensing data remains understudied due to the absence of suited datasets. We introduce a new dataset with aerial and satellite imagery in five countries with three forest-related regression tasks. To match real-world applicative interests, we compare methods through a restrictive setup where no prior on the target domain is available during training, and models are adapted with limited information during testing. Building on the assumption that ordered relationships generalize better, we propose manifold diffusion for regression as a strong baseline for transduction in low-data regimes. Our comparison highlights the comparative advantages of inductive and transductive methods in cross-domain regression.