Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring

• URL: http://arxiv.org/abs/2405.00514v2
• Date: Thu, 15 Aug 2024 12:48:12 GMT
• Title: Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring
• Authors: Sizhuo Li, Dimitri Gominski, Martin Brandt, Xiaoye Tong, Philippe Ciais,
• Abstract summary: We introduce a new dataset with aerial and satellite imagery in five countries with three forest-related regression tasks. We compare methods through a restrictive setup where no prior on the target domain is available during training. Building on the assumption that ordered relationships generalize better, we propose manifold diffusion for regression.
• Score: 1.4403877669472167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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