SelvaMask: Segmenting Trees in Tropical Forests and Beyond
This work addresses the challenge of scalable forest monitoring for ecological research and conservation, particularly in tropical regions, though it is incremental as it builds on existing vision foundation models.
The authors tackled the problem of accurately segmenting individual tree crowns in dense tropical forests using high-resolution aerial imagery, introducing SelvaMask, a dataset with over 8,800 manually delineated crowns, and a modular detection-segmentation pipeline that achieves state-of-the-art performance, outperforming existing methods.
Tropical forests harbor most of the planet's tree biodiversity and are critical to global ecological balance. Canopy trees in particular play a disproportionate role in carbon storage and functioning of these ecosystems. Studying canopy trees at scale requires accurate delineation of individual tree crowns, typically performed using high-resolution aerial imagery. Despite advances in transformer-based models for individual tree crown segmentation, performance remains low in most forests, especially tropical ones. To this end, we introduce SelvaMask, a new tropical dataset containing over 8,800 manually delineated tree crowns across three Neotropical forest sites in Panama, Brazil, and Ecuador. SelvaMask features comprehensive annotations, including an inter-annotator agreement evaluation, capturing the dense structure of tropical forests and highlighting the difficulty of the task. Leveraging this benchmark, we propose a modular detection-segmentation pipeline that adapts vision foundation models (VFMs), using domain-specific detection-prompter. Our approach reaches state-of-the-art performance, outperforming both zero-shot generalist models and fully supervised end-to-end methods in dense tropical forests. We validate these gains on external tropical and temperate datasets, demonstrating that SelvaMask serves as both a challenging benchmark and a key enabler for generalized forest monitoring. Our code and dataset will be released publicly.