Open-Canopy: Towards Very High Resolution Forest Monitoring

Fajwel Fogel, Yohann Perron, N. Besic, Laurent Saint-André, Agnès Pellissier-Tanon +6 more
2/9/2026

No official code found. Best implementation: fajwel/Open-Canopy (52 stars, Apache-2.0, last commit 2025-12-09).

Abstract

Estimating canopy height and its changes at meter resolution from satellite imagery remains a challenging computer vision task with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the reproducibility and evaluation of models. We introduce Open-Canopy, the first open-access, country-scale benchmark for very high-resolution (1.5 m) canopy height estimation, covering over 87,000 km2 across France with 1.5 m panchromatic resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-∆, a benchmark for canopy height reduction detection between images from different years at tree level—a difficult task for current computer vision models. We evaluate state-of-the-art architectures on these benchmarks, highlighting significant challenges and opportunities for improvement. Our datasets and code are publicly available at https://github.com/fajwel/Open-Canopy.

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Code Implementations(1)

fajwel/Open-CanopyCommunity100%
Apache-2.0

Canopy Height Estimation at very High Resolution

522Shell, PythonApr 24, 202410 months agoApache-2.0

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Cite this paper

@article{fogel2026opencanopy,
  title  = {Open-Canopy: Towards Very High Resolution Forest Monitoring},
  author = {Fajwel Fogel and Yohann Perron and N. Besic and Laurent Saint-André and Agnès Pellissier-Tanon and Martin Schwartz and Thomas Boudras and Ibrahim Fayad and Alexandre d'Aspremont and Loic Landrieu and Philippe Ciais},
  year   = {2026},
  eprint = {2407.09392},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2407.09392},
  journal = {CVPR 2024 2024}
}

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