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CHMv2: Improvements in Global Canopy Height Mapping using DINOv3

John Brandt, Seungeun Yi, Jamie Tolan, Xinyuan Li, Peter Potapov, Jessica Ertel, Justine Spore, Huy V. Vo, Michaël Ramamonjisoa, Patrick Labatut, Piotr Bojanowski, Camille Couprie

Abstract

Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measurements from airborne laser scanning (ALS) remain unevenly available globally. Here we present CHMv2, a global, meter-resolution canopy height map derived from high-resolution optical satellite imagery using a depth-estimation model built on DINOv3 and trained against ALS canopy height models. Compared to existing products, CHMv2 substantially improves accuracy, reduces bias in tall forests, and better preserves fine-scale structure such as canopy edges and gaps. These gains are enabled by a large expansion of geographically diverse training data, automated data curation and registration, and a loss formulation and data sampling strategy tailored to canopy height distributions. We validate CHMv2 against independent ALS test sets and against tens of millions of GEDI and ICESat-2 observations, demonstrating consistent performance across major forest biomes.

CHMv2: Improvements in Global Canopy Height Mapping using DINOv3

Abstract

Accurate canopy height information is essential for quantifying forest carbon, monitoring restoration and degradation, and assessing habitat structure, yet high-fidelity measurements from airborne laser scanning (ALS) remain unevenly available globally. Here we present CHMv2, a global, meter-resolution canopy height map derived from high-resolution optical satellite imagery using a depth-estimation model built on DINOv3 and trained against ALS canopy height models. Compared to existing products, CHMv2 substantially improves accuracy, reduces bias in tall forests, and better preserves fine-scale structure such as canopy edges and gaps. These gains are enabled by a large expansion of geographically diverse training data, automated data curation and registration, and a loss formulation and data sampling strategy tailored to canopy height distributions. We validate CHMv2 against independent ALS test sets and against tens of millions of GEDI and ICESat-2 observations, demonstrating consistent performance across major forest biomes.
Paper Structure (44 sections, 9 figures, 7 tables, 1 algorithm)

This paper contains 44 sections, 9 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Background & Summary
  2. Methods
  3. Overview of Workflow
  4. Input Imagery
  5. Decoder training and validation datasets
  6. NAIP-3DEP dataset
  7. SatLidar dataset
  8. Multisource dataset
  9. NAIP Sea dataset
  10. Training data curation
  11. Data cleaning
  12. Data registration
  13. Decoder loss functions
  14. SiLog loss.
  15. Charbonnier loss.
  16. ...and 29 more sections

Figures (9)

  • Figure 1: Visual improvements from CHMv1 to CHMv2 in a disturbance area in the Amazon (top), an urban forest in Central Java, Indonesia (middle) and a plantation in the Ghanaian cocoa belt (bottom).
  • Figure 2: Training and testing/validation data locations for NAIP-3DEP and SatLidar datasets.
  • Figure 3: Data registration methodology and analysis.
  • Figure 4: Compared to CHMv1 on NAIP-3DEP (top) and SatLidar v2 (bottom), CHMv2 exhibits greatly reduced biases, especially for trees above 30 m. The inset figures plot the 95th percentiles of CHM on $50\times50$ crops ($y$ axis) as a function of the 95th percentiles of Ground Truths ($x$ axis). Scales are in meters.
  • Figure 5: Qualitative improvements over CHMv1, in terms of sharpness and accuracy. Comparison on the NAIP-3DEP (two top lines) and SatLidar v2 (bottom lines) on 256$\times$256 samples. CHMv1 is without GEDI correction.
  • ...and 4 more figures