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| language:
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| - en
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| tags:
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| - chmv2
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| - dinov3
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| license_name: dinov3-license
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| license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
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| base_model: facebook/dinov3-vitl16-chmv2-dpt-head
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| pipeline_tag: depth-estimation
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| library_name: transformers
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| ---
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| # Model Card for CHMv2
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| The Canopy Height Maps v2 (CHMv2) model is a DPT-based decoder estimating canopy height given satellite imagery, leveraging DINOv3 as the backbone. Building on our original high-resolution canopy height maps released in 2024, CHMv2 delivers substantial improvements in accuracy, detail, and global consistency.
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|
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| ## Model Details
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| CHMv2 model was developed using the satellite DINOv3 ViT-L as the frozen backbone. Released with world-scale maps generated with it, they will help researchers and governments measure and understand every tree, gap, and canopy edge — enabling smarter biodiversity support and land-management decisions.
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|
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| ## Usage With Transformers
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| Run inference on an image with the following code:
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| ```python
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| from PIL import Image
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| import torch
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| from transformers import CHMv2ForDepthEstimation, CHMv2ImageProcessorFast
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| processor = CHMv2ImageProcessorFast.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head")
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| model = CHMv2ForDepthEstimation.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head")
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| image = Image.open("image.tif")
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| inputs = processor(images=image, return_tensors="pt")
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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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|
|
| ### Model Description
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| - **Developed by:** Meta AI
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| - **Model type:** DPT head
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| - **License:** [DINOv3 License](https://ai.meta.com/resources/models-and-libraries/dinov3-license/)
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|
|
| ### Model Sources
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|
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| - **Repository:** [https://github.com/facebookresearch/dinov3](https://github.com/facebookresearch/dinov3)
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| - **Paper:** [https://arxiv.org/abs/2603.06382](https://arxiv.org/abs/2603.06382)
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| ### Direct Use
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| The model can be used without fine-tuning to obtain competitive results on various satellite datasets ([paper link](https://arxiv.org/abs/2603.06382)).
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|
