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VibrantSR: Sub-Meter Canopy Height Models from Sentinel-2 Using Generative Flow Matching

arXiv 2601.09866 ↗First posted 14 Jan 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 33 references that are in the map. First posted on arXiv 14 Jan 2026.

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13 of 33 references.

References · 13

  1. 2022Flow Matching for Generative Modelingcited by 5,702
  2. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
  3. 2018Neural Ordinary Differential Equationscited by 3,779
  4. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  5. 2022A high-resolution canopy height model of the Earthcited by 91
  6. 2022A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecastscited by 66
  7. 2023Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidarcited by 60
  8. 2018Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural networkcited by 44
  9. 2020Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagerycited by 36
  10. 2022Increasing the accuracy and resolution of precipitation forecasts using deep generative modelscited by 35
  11. 2023High-Resolution Building and Road Detection from Sentinel-2cited by 15
  12. 2023Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Modelcited by 10
1 more
  1. 2024VibrantVS: A high-resolution multi-task transformer for forest canopy height estimationcited by 1

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  1. 2026FSKD: Monocular Forest Structure Inference via LiDAR-to-RGBI Knowledge Distillation
  2. 2025From Canopy to Ground via ForestGen3D: Learning Cross-Domain Generation of 3D Forest Structure from Aerial-to-Terrestrial LiDAR
  3. 2026Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
  4. 2025An Efficient Remote Sensing Super Resolution Method Exploring Diffusion Priors and Multi-Modal Constraints for Crop Type Mapping
  5. 2025Through-Foliage Surface-Temperature Reconstruction for Early Wildfire Detection
  6. 2026Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces
  7. 2026InfScene-SR: Seamless Super-Resolution of Arbitrarily Large Remote-Sensing Scenes via Variance-Preserving Joint Denoising
  8. 2024Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model