huggingface--transformers/docs/source/en/model_doc/chmv2.md

This model was published in HF papers on 2026-03-06 and contributed to Hugging Face Transformers on 2026-03-11.

CHMv2

Overview

The Canopy Height Maps v2 (CHMv2) model was proposed in CHMv2: Improvements in Global Canopy Height Mapping using DINOv3. Building on our original high-resolution canopy height maps released in 2024, CHMv2 delivers substantial improvements in accuracy, detail, and global consistency by leveraging DINOv3, Meta's self-supervised vision model.

You can find more information here, and the original code here.

The abstract from the paper is the following:

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.

Usage examples

Run inference on an image with the following code:

import torch
from PIL import Image

from transformers import AutoImageProcessor, AutoModelForDepthEstimation


processor = AutoImageProcessor.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head")
model = AutoModelForDepthEstimation.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head", device_map="auto")

image = Image.open("image.tif")
inputs = processor(images=image, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)

depth = processor.post_process_depth_estimation(
    outputs, target_sizes=[(image.height, image.width)]
)[0]["predicted_depth"]

CHMv2Config

autodoc CHMv2Config

CHMv2ImageProcessor

autodoc CHMv2ImageProcessor - preprocess - post_process_depth_estimation

CHMv2ForDepthEstimation

autodoc CHMv2ForDepthEstimation - forward