AIR-VIEW: The Aviation Image Repository for Visibility Estimation of Weather, A Dataset and Benchmark
This work addresses the need for low-cost weather sensors in aviation by providing a dataset and benchmark for visibility estimation, though it is incremental as it builds on existing methods.
The authors tackled the lack of publicly available datasets for aviation visibility estimation by introducing AIR-VIEW, a new dataset from a year-long FAA camera network collection, and provided benchmarks comparing three common approaches and a baseline against an ASTM standard.
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.