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language:
- en
tags:
- chmv2
- dinov3
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
base_model: facebook/dinov3-vitl16-chmv2-dpt-head
pipeline_tag: depth-estimation
library_name: transformers
---
# Model Card for CHMv2

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.

## Model Details

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.

## Usage With Transformers

Run inference on an image with the following code:

```python
from PIL import Image
import torch

from transformers import CHMv2ForDepthEstimation, CHMv2ImageProcessorFast

processor = CHMv2ImageProcessorFast.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head")
model = CHMv2ForDepthEstimation.from_pretrained("facebook/dinov3-vitl16-chmv2-dpt-head")

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

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

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

### Model Description

- **Developed by:** Meta AI
- **Model type:** DPT head
- **License:** [DINOv3 License](https://ai.meta.com/resources/models-and-libraries/dinov3-license/)

### Model Sources

- **Repository:** [https://github.com/facebookresearch/dinov3](https://github.com/facebookresearch/dinov3)
- **Paper:** [https://arxiv.org/abs/2603.06382](https://arxiv.org/abs/2603.06382)


### Direct Use

The model can be used without fine-tuning to obtain competitive results on various satellite datasets ([paper link](https://arxiv.org/abs/2603.06382)).