README.md

---
license: apache-2.0
---

# Depth Any Canopy Small

<!-- Provide a quick summary of what the model is/does. -->

This is the small version of Depth Any Canopy presented in Depth Any Canopy Paper. A [Base version](https://huggingface.co/DarthReca/depth-any-canopy-base) is also available.
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## Model Details

<!-- Provide a longer summary of what this model is. -->

The model is Depth-Anything-Small finetuned for canopy height estimation on a filtered set of [EarthView](https://huggingface.co/datasets/satellogic/EarthView).

- **License:** Apache 2.0
- **Finetuned from model:** [Depth-Anything-Small](https://huggingface.co/depth-anything/Depth-Anything-V2-Small-hf)

## Uses and Limitations

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

The model is capable of working with aerial imagery of NEON. The coverage is limited to the US. We cannot guarantee its generalizability over other areas of the globe.
The images cover only RGB channels; no study of hyperspectral imagery was done.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("depth-estimation", model="DarthReca/depth-any-canopy-base")

# Load model directly
from transformers import AutoModelForDepthEstimation

model = AutoModelForDepthEstimation.from_pretrained("DarthReca/depth-any-canopy-base")
```

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

- **Carbon Emitted:** 0.14 kgCO2

Carbon emissions are estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@inbook{RegeCambrin2025,
  title = {Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation},
  ISBN = {9783031923876},
  ISSN = {1611-3349},
  url = {http://dx.doi.org/10.1007/978-3-031-92387-6_5},
  DOI = {10.1007/978-3-031-92387-6_5},
  booktitle = {Computer Vision – ECCV 2024 Workshops},
  publisher = {Springer Nature Switzerland},
  author = {Rege Cambrin,  Daniele and Corley,  Isaac and Garza,  Paolo},
  year = {2025},
  pages = {71–86}
}
```