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--- |
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license: apache-2.0 |
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--- |
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# Depth Any Canopy Base |
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<!-- Provide a quick summary of what the model is/does. --> |
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This is the base version of Depth Any Canopy presented in Depth Any Canopy Paper. A [Small version](https://huggingface.co/DarthReca/depth-any-canopy-small) is also available. |
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## Model Details |
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<!-- Provide a longer summary of what this model is. --> |
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The model is Depth-Anything-Base finetuned for canopy height estimation on a filtered set of [EarthView](https://huggingface.co/datasets/satellogic/EarthView). |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** [Depth-Anything-Base](https://huggingface.co/depth-anything/Depth-Anything-V2-Base-hf) |
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## Uses and Limitations |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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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. |
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The images cover only RGB channels; no study of hyperspectral imagery was done. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("depth-estimation", model="DarthReca/depth-any-canopy-base") |
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# Load model directly |
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from transformers import AutoModelForDepthEstimation |
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model = AutoModelForDepthEstimation.from_pretrained("DarthReca/depth-any-canopy-base") |
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``` |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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- **Carbon Emitted:** 0.14 kgCO2 |
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Carbon emissions are estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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``` |
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@inbook{RegeCambrin2025, |
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title = {Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation}, |
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ISBN = {9783031923876}, |
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ISSN = {1611-3349}, |
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url = {http://dx.doi.org/10.1007/978-3-031-92387-6_5}, |
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DOI = {10.1007/978-3-031-92387-6_5}, |
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booktitle = {Computer Vision – ECCV 2024 Workshops}, |
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publisher = {Springer Nature Switzerland}, |
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author = {Rege Cambrin, Daniele and Corley, Isaac and Garza, Paolo}, |
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year = {2025}, |
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pages = {71–86} |
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} |
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``` |
