# Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery

arXiv: https://arxiv.org/abs/2503.20199 · HTML: https://mlsanity.com/paper/2503.20199/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot](https://mlsanity.com/paper/2306.16623/) (2023)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2304.09148/) (2023)
- [ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery](https://mlsanity.com/paper/2201.11192/) (2022)

## Built on by (most cited first, 4 of 4)

- [SelvaBox: A high-resolution dataset for tropical tree crown detection](https://mlsanity.com/paper/2507.00170/) (2025)
- [FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations](https://mlsanity.com/paper/2602.00470/) (2026)
- [Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels](https://mlsanity.com/paper/2602.13022/) (2026)
- [Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery](https://mlsanity.com/paper/2605.15673/) (2026)

## Abstract (arXiv)

The potential of tree planting as a natural climate solution is often undermined by inadequate monitoring of tree planting projects. Current monitoring methods involve measuring trees by hand for each species, requiring extensive cost, time, and labour. Advances in drone remote sensing and computer vision offer great potential for mapping and characterizing trees from aerial imagery, and large pre-trained vision models, such as the Segment Anything Model (SAM), may be a particularly compelling choice given limited labeled data. In this work, we compare SAM methods for the task of automatic tree crown instance segmentation in high resolution drone imagery of young tree plantations. We explore the potential of SAM for this task, and find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts, but that there is potential for methods which tune SAM further. We also show that predictions can be improved by adding Digital Surface Model (DSM) information as an input.