  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 with 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.
