用多任务Transformer提升森林树冠高度估测精度与更新速度
VibrantVS: A high-resolution multi-task transformer for forest canopy height estimation
- 基于四波段NAIP影像，设计多任务视觉变换模型
- 在西部生态区平均精度更高，三年内可完成更新
- 适合生态监测与林火防控等需要高频数据的场景
本文探索了一种新型多任务视觉变换器（ViT）模型在使用美国西部地区四波段国家农业影像计划（NAIP）影像估算树冠高程模型（CHMs）中的应用。通过对比该模型在不同生态区和树高类别下的准确率与精确度，发现尽管其他基准模型在局部区域具有较高精度，但VibrantVS模型在覆盖范围更广的西部生态区表现出更高的整体准确率与精确度，支持每三年或更短周期的更新，并具备高空间分辨率。该模型为生态监测与土地管理决策，包括林火预防，提供了重要价值。
原文摘要 · Abstract (English)
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.
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