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

cs.CVMar 6, 2026

CHMv2: Improvements in Global Canopy Height Mapping using DINOv3

Authors: John Brandt, Seungeun Yi, Jamie Tolan, Xinyuan Li, Peter Potapov, Jessica Ertel, Justine Spore, Huy V. Vo,

Organizations: World Resources Institute, 10 G St NE #800, Washington, DC 20002, USA · Fundamental AI Research (FAIR), Meta, 75002 Paris, France · Meta, 1 Hacker Way, Menlo Park, CA 94025, USA · University of Maryland, Department of Geography, College Park, MD 20742, USA

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.

Figures & tables

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.

Panel of Figure 3 — Figure 3: Data registration methodology and analysis.

Panel of Figure 4 — 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 $50\times 50$ crops ( $y$ axis) as a function of the 95th percentiles of Ground Truths ( $x$ axis). Scales are in meters.

Panel of Figure 5 — 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.

Panel of Figure 6 — Figure 6: Comparison of CHMv1 and CHMv2 on the Multisource dataset. CHMv1 is without GEDI correction.

Figure 7: Canopy height profiles along a single 10-m wide transect comparing ALS reference measurements to five canopy height datasets. Gray points show ALS returns within the transect corridor; the black line shows the ALS-derived canopy height profile. ALS data is from Kalimantan, Indonesia (1.6412

\degree

, 115.281

\degree

) and is

\geq 200

km from any observation within the SatLidar v2 dataset.

Dataset	Resolution (m)	MAE
Pauls et al. [ 39 ]	10	7.5
Lang et al. [ 26 ]	10	8.4
Potapov et al. [ 40 ]	30	4.9
CHMv1 [ 50 ]	1	4.3
CHMv2	1	3.0

Table 1: Comparison of global canopy height products to ground truth ALS-derived CHM data in the SatLidar v2 test split.

Panel of Figure 8 — Figure 8: Comparison with GEDI and ICESat-2.

Backbone	Setting	Loss	Sampling	Train data	SatLidar v2				NAIP-3DEP
Backbone	Setting	Loss	Sampling	Train data	MAE	$R^{2}$	Bias	Edge	MAE	$R^{2}$	Edge
CHMv1	Base	SiLog	Uniform	Neon train	4.3	0.53	2.6	0.67	2.2	0.54	0.77
DINOv3	Base	SiLog	Uniform	Neon train	3.9	0.64	2.2	0.66	2.0	0.59	0.72
DINOv3	Base	SiLog	Uniform	SatLidar v1 train	3.5	0.78	0.9	0.62	2.0	0.46	0.63
DINOv3	Base	SiLog	Uniform	SatLidar v2 train	3.5	0.76	0.5	0.58	2.1	0.52	0.63
DINOv3	Final	Cur.	Uniform	SatLidar v2 train	3.5	0.75	-0.5	0.55	2.2	0.48	0.58

Table 2: Main ablation from the CHMv1 settings to the CHMv2 settings, demonstrating improvements due to the DINOv3 backbone, training data diversity, registration, new architecture and loss. The base setting corresponds to the CHMv1 architecture and training parameters, and the final setting to the changes described in Section 2.6 .

Panel of Figure 9 — Figure 9: Impact of our loss, a Combination of SiLog, Charbonnier and Multi-scale Patch Gradient on the NAIP-3DEP results. Compared to SiLog loss, results are sharper and more accurate.

Table 12

Table 13

Category sampling	NAIP-3DEP	SatLidar v2	SatLidar v2		NAIP-3DEP		Multisource
Category sampling	NAIP-3DEP	SatLidar v2	MAE	$R^{2}$	MAE	$R^{2}$	MAE	$R^{2}$
✗	50 $\%$	50 $\%$	3.07	0.83	1.37	0.93	2.98	0.77
✓	50 $\%$	50 $\%$	3.07	0.84	1.36	0.93	2.98	0.78
✓	30 $\%$	70 $\%$	3.10	0.83	1.38	0.93	2.94	0.82
✓	70 $\%$	30 $\%$	3.18	0.82	1.35	0.93	2.98	0.79

Table 7: Data mix ablation: using a 50-50 ratio of data source brings a good compromise. Category sampling shows slightly improved metrics. To complement this ablation, we computed that the category sampling improves the MAE of trees above 35 meters by at least two meters. Using DINOv3 viT-L backbone, Charbonnier + Gradient Loss.

Remote Sensing

Remote sensing uses satellite, aerial, and ground-based observations to measure and map Earth and other planetary surfaces.

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ECHOSAT: Estimating Canopy Height Over Space And Time

Jan Pauls, Karsten Schrödter, Sven Ligensa, Martin Schwartz+6

Department of Information Systems, University of Münster, Germany · Laboratoire des Sciences du Climat et de l’Environnement (LSCE), France · Zuse Institute Berlin (ZIB), Germany+2

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Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We introduce ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years. To this end, we resort to multi-sensor satellite data to train a specialized vision transformer model, which performs pixel-level temporal regression. A self-supervised growth loss regularizes the predictions to follow growth curves that are in line with natural tree development, including gradual height increases over time, but also abrupt declines due to forest loss events such as fires. Our experimental evaluation shows that our model improves state-of-the-art accuracies in the context of single-year predictions. We also provide the first global-scale height map that accurately quantifies tree growth and disturbances over time. We expect ECHOSAT to advance global efforts in carbon monitoring and disturbance assessment. The maps can be accessed at https://github.com/ai4forest/echosat.

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