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.

翻译：图像级回归是地球观测中的一项重要任务，其中视觉域和标签偏移是阻碍泛化的核心挑战。然而，由于缺乏合适的数据集，利用遥感数据进行跨域回归的研究仍不充分。我们引入了一个包含五个国家航拍和卫星图像的新数据集，涵盖三项与森林相关的回归任务。为匹配实际应用需求，我们通过限制性设置比较了不同方法——在训练期间无法获取目标域先验信息，且在测试期间仅利用有限信息进行模型适配。基于有序关系更易泛化的假设，我们提出用于回归的流形扩散方法，作为低数据场景下传导性学习的强大基线。我们的比较凸显了归纳式与传导式方法在跨域回归中的相对优势。