Open-Canopy: Towards Very High Resolution Forest Monitoring
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909424740728832 |
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| author | Fogel, Fajwel Perron, Yohann Besic, Nikola Saint-André, Laurent Pellissier-Tanon, Agnès Schwartz, Martin Boudras, Thomas Fayad, Ibrahim d'Aspremont, Alexandre Landrieu, Loic Ciais, Philippe |
| author_facet | Fogel, Fajwel Perron, Yohann Besic, Nikola Saint-André, Laurent Pellissier-Tanon, Agnès Schwartz, Martin Boudras, Thomas Fayad, Ibrahim d'Aspremont, Alexandre Landrieu, Loic Ciais, Philippe |
| contents | Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision 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 km$^2$ across France with 1.5 m resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-$Δ$, a benchmark for canopy height change detection between images from different years at tree level-a challenging 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open-Canopy: Towards Very High Resolution Forest Monitoring Fogel, Fajwel Perron, Yohann Besic, Nikola Saint-André, Laurent Pellissier-Tanon, Agnès Schwartz, Martin Boudras, Thomas Fayad, Ibrahim d'Aspremont, Alexandre Landrieu, Loic Ciais, Philippe Computer Vision and Pattern Recognition Image and Video Processing Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision 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 km$^2$ across France with 1.5 m resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-$Δ$, a benchmark for canopy height change detection between images from different years at tree level-a challenging 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. |
| title | Open-Canopy: Towards Very High Resolution Forest Monitoring |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2407.09392 |