Communication Dans Un Congrès Année : 2025

Open-Canopy: Towards Very High Resolution Forest Monitoring

Résumé

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-$\Delta$, 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 \url{https://github.com/fajwel/Open-Canopy}.

Fichier non déposé

Dates et versions

hal-04710775 , version 1 (26-09-2024)

Identifiants

Citer

Fajwel Fogel, Yohann Perron, Nikola Besic, Laurent Saint-André, Agnès Pellissier-Tanon, et al.. Open-Canopy: Towards Very High Resolution Forest Monitoring. 2025 Conference on Computer Vision and Pattern Recognition, Jun 2025, Nashville, United States. ⟨10.1109/CVPR52734.2025.00138⟩. ⟨hal-04710775⟩
1025 Consultations
0 Téléchargements

Altmetric

Partager

  • More