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Developed by: Meta AI
Model type: DPT head
License: <a href="https://ai.meta.com/resources/models-and-libraries/dinov3-license/" rel="nofollow">DINOv3 License

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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:

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"]