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73 lines
3.8 KiB
Markdown
73 lines
3.8 KiB
Markdown
<!--Copyright 2026 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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*This model was published in HF papers on 2026-03-06 and contributed to Hugging Face Transformers on 2026-03-11.*
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# CHMv2
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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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.
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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).
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The abstract from the paper is the following:
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*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.*
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## Usage examples
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Run inference on an image with the following code:
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```python
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import torch
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from PIL import Image
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from transformers import AutoImageProcessor, AutoModelForDepthEstimation
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processor = AutoImageProcessor.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head")
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model = AutoModelForDepthEstimation.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head", device_map="auto")
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image = Image.open("image.tif")
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inputs = processor(images=image, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model(**inputs)
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depth = processor.post_process_depth_estimation(
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outputs, target_sizes=[(image.height, image.width)]
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)[0]["predicted_depth"]
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```
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## CHMv2Config
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[[autodoc]] CHMv2Config
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## CHMv2ImageProcessor
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[[autodoc]] CHMv2ImageProcessor
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- preprocess
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- post_process_depth_estimation
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## CHMv2ForDepthEstimation
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[[autodoc]] CHMv2ForDepthEstimation
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- forward
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