Uncertainty-aware tree height change regression

Monitoring canopy height change is essential for understand-ing carbon sinks and forest dynamics. Remote sensing enables consis-tent, large-scale observations of such changes, increasingly integratedwith deep learning architectures such as Geospatial Foundation Models(GFMs). However, existing methods and datasets frame the problem asbinary change detection, which overlooks both the continuous nature ofchange, especially for vegetation, and the inherent uncertainty in labels.We present the Canopy Height Change (CHC) dataset, providing 3 mresolution continuous canopy height differences and associated spatiallyresolved uncertainties across 10 598 km2 of northern and western Spain.The dataset is paired with a co-located time series of PlanetScope satel-lite imagery. Based on the dataset, we introduce the task of uncertainty-aware change regression, associated metrics and strategies for fine-tuningGFMs. Furthermore, we evaluate state-of-the-art GFMs and highlightpromising directions and remaining challenges for advancing continuouscanopy height change estimation.