构建首个航空气象能见度图像数据集，助力低成本天气感知
AIR-VIEW: The Aviation Image Repository for Visibility Estimation of Weather, A Dataset and Benchmark
- 从联邦航空局摄像头网络采集一年图像，覆盖多样地理场景
- 在三个公开数据集上验证模型，符合最新ASTM标准
- 为航空能见度估计提供首个可比基准，适合计算机视觉研究者
航空气象的机器学习研究正兴起，旨在为传统昂贵传感器提供低成本替代方案；然而，在大气能见度估计领域，缺乏足够规模、标注准确、涵盖多元地理位置且适用于监督学习的公开数据集。本文提出一个新数据集，汇集了为期一年的美国联邦航空局（FAA）天气摄像头网络图像，专为该任务设计。我们还建立了一个基准，对比三种常用方法，并在三个公开数据集（包括自建数据集）上训练和测试通用基线模型，结果与最近通过的ASTM标准进行对比。
原文摘要 · Abstract (English)
Machine Learning for aviation weather is a growing area of research for providing low-cost alternatives for traditional, expensive weather sensors; however, in the area of atmospheric visibility estimation, publicly available datasets, tagged with visibility estimates, of distances relevant for aviation, of diverse locations, of sufficient size for use in supervised learning, are absent. This paper introduces a new dataset which represents the culmination of a year-long data collection campaign of images from the FAA weather camera network suitable for this purpose. We also present a benchmark when applying three commonly used approaches and a general-purpose baseline when trained and tested on three publicly available datasets, in addition to our own, when compared against a recently ratified ASTM standard.
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