VibrantVS: A high-resolution multi-task transformer for forest canopy height estimation

TL;DR

VibrantVS is a novel high-resolution multi-task transformer model that improves forest canopy height estimation accuracy and precision across diverse ecoregions using NAIP imagery, supporting ecological and land management applications.

Contribution

The paper introduces VibrantVS, a multi-task vision transformer that outperforms existing models in forest canopy height estimation across broad geographic regions.

Findings

VibrantVS achieves higher accuracy and precision than benchmark models.

The model enables updated canopy height inference every three years or less.

It provides high spatial resolution suitable for ecological monitoring and wildfire mitigation.

Abstract

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.