AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training

Year 2024, , 735 - 746, 30.09.2024

Nesil Bor , Nergis Pervan Akman , Ali Berkol

https://doi.org/10.28979/jarnas.1500916

Abstract

Acquiring a sufficient amount of diverse and accurate real-world data poses a significant challenge in advancing autonomous systems, which are becoming increasingly popular. Despite the aerospace industry's keen practical and economic interest in autonomous landing systems, readily available open-source datasets containing aerial photographs are scarce. To address this issue, we present a dataset named AeroRunway, comprising high-quality aerial photos designed to aid in runway recognition during the approach and landing stages. The dataset is composed of images using X-Plane, a flight simulator software developed by Laminar Research. It is a highly realistic and detailed flight simulation program that allows users to experience the sensation of piloting various aircraft in a virtual environment. These synthetic images were collected mostly in variable weather conditions above 5000 feet to supplement existing satellite imagery that can be used for extreme situations. This dataset was created from 28 different airports in different weather conditions, such as foggy and rainy, at various times of the day, such as day and night, and consists of 3880 images and is approximately 13.3 GB in size.

Keywords

Aerodrome detection, spatial awareness, artificial intelligence, deep learning, machine learning

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