TREEUQ: UNCERTAINTY-AWARE MULTIMODAL REGRESSION FOR TREE STRUCTURE

Accurate characterization of forest structure is essential for understanding the global carbon cycle and ecosystem dynamics. While high resolution aerial imagery has enabled large-scale mapping of continuous forest attributes like tree segmentation, fundamental ecological quantities such as tree height and density remain largely absent from open satellite resolution (Sentinel-1 and Sentinel-2) benchmarks. In this work, we introduce TreeUQ, a multimodal Earth observation benchmark for tree-structure estimation over Bavaria, Germany. The dataset contains Sentinel-1 and Sentinel-2 composites, high resolution RGB (20cm), tree-species information, and six tree-structure related targets. We benchmark mean tree-height and density estimation, and compare methods for uncertainty quantification. Modality ablations assess the contributions of radar, optical, and species information. We establish rigorous evaluation protocols using a spatially blocked split strategy to prevent geographic data leakage.