# SelvaMask: Segmenting Trees in Tropical Forests and Beyond

arXiv: https://arxiv.org/abs/2602.02426 · HTML: https://mlsanity.com/paper/2602.02426/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [Application-Driven Innovation in Machine Learning](https://mlsanity.com/paper/2403.17381/) (2024)
- [OpenForest: A data catalogue for machine learning in forest monitoring](https://mlsanity.com/paper/2311.00277/) (2023)
- [OAM-TCD: A globally diverse dataset of high-resolution tree cover maps](https://mlsanity.com/paper/2407.11743/) (2024)
- [ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds](https://mlsanity.com/paper/2506.16991/) (2025)
- [Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery](https://mlsanity.com/paper/2506.04970/) (2025)
- [SelvaBox: A high-resolution dataset for tropical tree crown detection](https://mlsanity.com/paper/2507.00170/) (2025)
- [Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation](https://mlsanity.com/paper/2409.08171/) (2024)
- [Zero-Shot Tree Detection and Segmentation from Aerial Forest Imagery](https://mlsanity.com/paper/2506.03114/) (2025)
- [Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation](https://mlsanity.com/paper/2505.23597/) (2025)

## Built on by (most cited first, 5 of 5)

- [Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping](https://mlsanity.com/paper/2605.05627/) (2026)
- [Estimating Individual Tree Height and Species from UAV Imagery](https://mlsanity.com/paper/2603.23669/) (2026)
- [deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection](https://mlsanity.com/paper/2605.19605/) (2026)
- [Toward a foundation model for forest point clouds](https://mlsanity.com/paper/2609.24787/) (2026)
- [Latent Commonality Expectation-Maximisation for Box-supervised Tree Crown Instance Segmentation](https://mlsanity.com/paper/2609.26549/) (2026)

## Abstract (arXiv)

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.