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arxiv logo>cs> arXiv:2412.10351
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2412.10351 (cs)
[Submitted on 13 Dec 2024 (v1), last revised 24 Jan 2025 (this version, v3)]

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

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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.
Comments:15 pages, 12 figures
Subjects:Computer Vision and Pattern Recognition (cs.CV)
MSC classes:I.2.10
Cite as:arXiv:2412.10351 [cs.CV]
 (orarXiv:2412.10351v3 [cs.CV] for this version)
 https://doi.org/10.48550/arXiv.2412.10351
arXiv-issued DOI via DataCite

Submission history

From: Andreas Gros [view email]
[v1] Fri, 13 Dec 2024 18:47:11 UTC (37,087 KB)
[v2] Mon, 13 Jan 2025 12:22:52 UTC (31,246 KB)
[v3] Fri, 24 Jan 2025 19:38:09 UTC (25,766 KB)
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