Dargana: fine-tuning EarthPT for dynamic tree canopy mapping from space
This study addresses the need for efficient natural capital management by proposing a low-overhead, high-accuracy method for dynamic canopy cover monitoring. Methodologically, it leverages the EarthPT temporal foundation model, applying lightweight supervised fine-tuning using <3% of its pretraining data and only 5% of the original computational budget, while integrating multi-temporal Sentinel-2 imagery to achieve 10-m pixel-level semantic segmentation. The framework supports coniferous/deciduous forest classification, as well as fine-grained structural identification (e.g., hedgerows, shrubs) and detection of dynamic changes such as new afforestation. Its key contribution lies in being the first to specialize EarthPT for fine-grained vegetation dynamics monitoring under extreme data and compute constraints—enabling sub-pixel object recognition and quantitative time-series analysis despite limited annotations. Evaluated in Cornwall, UK, it achieves ROC-AUC = 0.98 and PR-AUC = 0.83, and generalizes successfully to unseen fine structures and novel change types.