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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.09866 |
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| _version_ | 1866918316836126720 |
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| author | Ndegwa, Kiarie Gros, Andreas Chang, Tony Diaz, David Landau, Vincent A. Rutenbeck, Nathan E. Zachmann, Luke J. Bayes, Guy Conway, Scott |
| author_facet | Ndegwa, Kiarie Gros, Andreas Chang, Tony Diaz, David Landau, Vincent A. Rutenbeck, Nathan E. Zachmann, Luke J. Bayes, Guy Conway, Scott |
| contents | We present VibrantSR (Vibrant Super-Resolution), a generative super-resolution framework for estimating 0.5 meter canopy height models (CHMs) from 10 meter Sentinel-2 imagery. Unlike approaches based on aerial imagery that are constrained by infrequent and irregular acquisition schedules, VibrantSR leverages globally available Sentinel-2 seasonal composites, enabling consistent monitoring at a seasonal-to-annual cadence. Evaluated across 22 EPA Level 3 eco-regions in the western United States using spatially disjoint validation splits, VibrantSR achieves a Mean Absolute Error of 4.39 meters for canopy heights >= 2 m, outperforming Meta (4.83 m), LANDFIRE (5.96 m), and ETH (7.05 m) satellite-based benchmarks. While aerial-based VibrantVS (2.71 m MAE) retains an accuracy advantage, VibrantSR enables operational forest monitoring and carbon accounting at continental scales without reliance on costly and temporally infrequent aerial acquisitions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09866 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VibrantSR: Sub-Meter Canopy Height Models from Sentinel-2 Using Generative Flow Matching Ndegwa, Kiarie Gros, Andreas Chang, Tony Diaz, David Landau, Vincent A. Rutenbeck, Nathan E. Zachmann, Luke J. Bayes, Guy Conway, Scott Computer Vision and Pattern Recognition Machine Learning We present VibrantSR (Vibrant Super-Resolution), a generative super-resolution framework for estimating 0.5 meter canopy height models (CHMs) from 10 meter Sentinel-2 imagery. Unlike approaches based on aerial imagery that are constrained by infrequent and irregular acquisition schedules, VibrantSR leverages globally available Sentinel-2 seasonal composites, enabling consistent monitoring at a seasonal-to-annual cadence. Evaluated across 22 EPA Level 3 eco-regions in the western United States using spatially disjoint validation splits, VibrantSR achieves a Mean Absolute Error of 4.39 meters for canopy heights >= 2 m, outperforming Meta (4.83 m), LANDFIRE (5.96 m), and ETH (7.05 m) satellite-based benchmarks. While aerial-based VibrantVS (2.71 m MAE) retains an accuracy advantage, VibrantSR enables operational forest monitoring and carbon accounting at continental scales without reliance on costly and temporally infrequent aerial acquisitions. |
| title | VibrantSR: Sub-Meter Canopy Height Models from Sentinel-2 Using Generative Flow Matching |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2601.09866 |