# Uncertainty-aware tree height change regression, v1.0 ## Introduction Monitoring canopy height change is essential for understanding carbon sinks and forest dynamics. Remote sensing enables consistent, large-scale observations of such changes, increasingly integrated with deep learning architectures such as Geospatial Foundation Models (GFMs). However, existing methods and datasets frame the problem as binary change detection, which overlooks both the continuous nature of change, especially for vegetation, and the inherent uncertainty in labels. We present the *Canopy Height Change (CHC)* dataset, providing 3 m resolution *continuous* canopy height differences and associated spatially resolved uncertainties across 10,598 km² of northern and western Spain. The dataset is paired with a co-located time series of PlanetScope satellite imagery. Based on the dataset, we introduce the task of uncertainty-aware change regression, associated metrics and strategies for fine-tuning GFMs. Furthermore, we evaluate state-of-the-art GFMs and highlight promising directions and remaining challenges for advancing continuous canopy height change estimation. ## Setup The CHC dataset is designed to be integrated into PANGAEA (). 1. Place `chc.py` into `pangaea/datasets` 2. Place `chc.yaml` into `configs/dataset` 3. Run the benchmark with `dataset=chc` The dataset will automatically download (alternatively, it is available in the ZIP archive). ## Terms of use for the PNOA LiDAR-derived products (Canopy height change maps) Derivative work of LiDAR-PNOA-cob2 2018 CC-BY 4.0 and LiDAR-PNOA-cob3 2022–2025 CC-BY 4.0 scne.es ## Terms of use for PlanetScope imagery Any usage must be solely for Noncommercial education or scientific research purposes, and publication in academic or scientific research journals. All such publications must include an attribution that clearly and conspicuously identifies Planet Labs PBC. ## Known issues - Change artifacts from point misclassifications (e.g., buildings) ``