Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation

Daniele Rege Cambrin¹ · Isaac Corley² · Paolo Garza¹

¹Politecnico di Torino, Italy    ²University of Texas at San Antonio, USA

ECCV 2024 CV4E Workshop

In this paper, we propose transferring the representations learned by recent depth estimation foundation models to the remote sensing domain for measuring canopy height. Our findings suggest that our proposed Depth Any Canopy, the result of fine-tuning the Depth Anything v2 model for canopy height estimation, provides a performant and efficient solution, surpassing the current state-of-the-art with superior or comparable performance using only a fraction of the computational resources and parameters. Furthermore, our approach requires less than $1.30 in compute and results in an estimated carbon footprint of 0.14 kgCO2.

REPOSITORY IN CONSTRUCTION SOME FILES COULD BE MISSING

Getting Started

Install the dependencies of the requirements.txt file. Make sure to edit the config files in the configs/ folder. Then simply run main.py

Pre-Trained Models

Pre-trained checkpoints are available on HuggingFace.

Model	Parameters	Checkpoint
Depth-Any-Canopy-Small	24.8M	Download
Depth-Any-Canopy-Base	97.5M	Download

You can easily load them with pipelines or AutoModel:

# 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")

License

This project is licensed under the Apache 2.0 license. See LICENSE for more information.

Citation

If you find this project useful, please consider citing:

@misc{cambrin2024depthcanopyleveragingdepth,
      title={Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation}, 
      author={Daniele Rege Cambrin and Isaac Corley and Paolo Garza},
      year={2024},
      eprint={2408.04523},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.04523}, 
}