This paper explores the application of a novel multi-task vision transformer (ViT) model for the estimation of canopy height models (CHMs) using 4-band National Agriculture Imagery Program (NAIP) imagery across the western United States. We compare the effectiveness of this model in terms of accuracy and precision aggregated across ecoregions and class heights versus three other benchmark peer-reviewed models. Key findings suggest that, while other benchmark models can provide high precision in localized areas, the VibrantVS model has substantial advantages across a broad reach of ecoregions in the western United States with higher accuracy, higher precision, the ability to generate updated inference at a cadence of three years or less, and high spatial resolution. The VibrantVS model provides significant value for ecological monitoring and land management decisions, including for wildfire mitigation.
Cite
@article{arxiv.2412.10351,
title = {VibrantVS: A high-resolution multi-task transformer for forest canopy height estimation},
author = {Tony Chang and Kiarie Ndegwa and Andreas Gros and Vincent A. Landau and Luke J. Zachmann and Bogdan State and Mitchell A. Gritts and Colton W. Miller and Nathan E. Rutenbeck and Scott Conway and Guy Bayes},
journal= {arXiv preprint arXiv:2412.10351},
year = {2025}
}