SelvaMask: Segmenting Trees in Tropical Forests and Beyond
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866910009026150400 |
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| author | Duguay, Simon-Olivier Baudchon, Hugo Laliberté, Etienne Muller-Landau, Helene Rivas-Torres, Gonzalo Ouaknine, Arthur |
| author_facet | Duguay, Simon-Olivier Baudchon, Hugo Laliberté, Etienne Muller-Landau, Helene Rivas-Torres, Gonzalo Ouaknine, Arthur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02426 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SelvaMask: Segmenting Trees in Tropical Forests and Beyond Duguay, Simon-Olivier Baudchon, Hugo Laliberté, Etienne Muller-Landau, Helene Rivas-Torres, Gonzalo Ouaknine, Arthur Computer Vision and Pattern Recognition I.2.10; I.4.8; I.4.9; I.5.4 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. |
| title | SelvaMask: Segmenting Trees in Tropical Forests and Beyond |
| topic | Computer Vision and Pattern Recognition I.2.10; I.4.8; I.4.9; I.5.4 |
| url | https://arxiv.org/abs/2602.02426 |