标题： AIR-VIEW：天气能见度估计的航空图像库，一个数据集和基准 显示英文标题

标题： AIR-VIEW: The Aviation Image Repository for Visibility Estimation of Weather, A Dataset and Benchmark

摘要： 机器学习在航空天气领域是一个快速增长的研究领域，旨在为传统昂贵的天气传感器提供低成本的替代方案；然而，在大气能见度估计领域，缺乏公开可用的数据集，这些数据集带有与航空相关的距离能见度估计，涵盖多样化的位置，并且规模足够大以用于监督学习。 本文介绍了一个新的数据集，该数据集是为期一年的美国联邦航空管理局天气摄像头网络图像收集活动的成果，适用于此目的。 我们还展示了在应用三种常用方法时的基准，以及在三个公开可用数据集（包括我们自己的数据集）上训练和测试时的通用基线，与最近批准的ASTM标准进行比较。 显示英文摘要

摘要： 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.