Arthur Ouaknine

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery

Mélisande Teng

Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.

2025-09-17

NeurIPS.cc/2025/Conference (poster)

doi.org

openreview.net

Tree semantic segmentation from aerial image time series

Venkatesh Ramesh

Arthur Ouaknine

David Rolnick

2025-07-29

Environmental Data Science (publié)

doi.org

arxiv.org

Tree semantic segmentation from aerial image time series

Venkatesh Ramesh

Arthur Ouaknine

David Rolnick

2025-07-29

Environmental Data Science (publié)

doi.org

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery

Mélisande Teng

Etienne Lalibert'e

Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.

2025-06-05

ArXiv (prépublication)

arxiv.org

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery

Mélisande Teng

Etienne Lalibert'e

Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.

2025-06-01

arXiv (publié)

doi.org

arxiv.org

FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring

Nikolaos Ioannis Bountos

Arthur Ouaknine

Ioannis Papoutsis

David Rolnick

2025-04-11

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org