*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](https://huggingface.co/papers/2603.06382). Building on our [original high-resolution canopy height maps](https://sustainability.atmeta.com/blog/2024/04/22/using-artificial-intelligence-to-map-the-earths-forests/) 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](http://ai.meta.com/blog/world-resources-institute-dino-canopy-height-maps-v2), and the original code [here](https://github.com/facebookresearch/dinov3). 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: ```python 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