Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation
This provides an efficient and low-cost method for forest conservation and climate change applications, though it is incremental as it adapts an existing foundation model to a new domain.
The paper tackles the problem of estimating global tree canopy height from single-view remotely sensed imagery by fine-tuning the Depth Anything v2 model, resulting in a solution that surpasses the state-of-the-art with superior or comparable performance using only a fraction of computational resources and parameters, costing less than $1.30 in compute and emitting 0.14 kgCO2.
Estimating global tree canopy height is crucial for forest conservation and climate change applications. However, capturing high-resolution ground truth canopy height using LiDAR is expensive and not available globally. An efficient alternative is to train a canopy height estimator to operate on single-view remotely sensed imagery. The primary obstacle to this approach is that these methods require significant training data to generalize well globally and across uncommon edge cases. Recent monocular depth estimation foundation models have show strong zero-shot performance even for complex scenes. In this paper we leverage the representations learned by these models to transfer 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. Code, experimental results, and model checkpoints are openly available at https://github.com/DarthReca/depth-any-canopy.