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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.*
+# CHMv2
+
+<div class="flex flex-wrap space-x-1">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## 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