AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training
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 op...
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| Format: | Article |
| Language: | English |
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Çanakkale Onsekiz Mart University
2024-09-01
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| Series: | Journal of Advanced Research in Natural and Applied Sciences |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/4004340 |
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| _version_ | 1832095389975576576 |
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| author | Ali Berkol Nergis Pervan Akman Nesil Bor |
| author_facet | Ali Berkol Nergis Pervan Akman Nesil Bor |
| author_sort | Ali Berkol |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7276b8e6e2af434e8c541f308abe14ce |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-7276b8e6e2af434e8c541f308abe14ce2025-02-05T18:13:03ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952024-09-0110373574610.28979/jarnas.1500916453AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing TrainingAli Berkol0https://orcid.org/0000-0002-3056-1226Nergis Pervan Akman1https://orcid.org/0000-0003-3241-6812Nesil Bor2https://orcid.org/0009-0001-2882-1471BITES Defense and Information SystemsBITES Defense and Information SystemsORTA DOĞU TEKNİK ÜNİVERSİTESİAcquiring 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.https://dergipark.org.tr/en/download/article-file/4004340aerodrome detectionspatial awarenessartificial intelligencedeep learningmachine learning |
| spellingShingle | Ali Berkol Nergis Pervan Akman Nesil Bor AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training Journal of Advanced Research in Natural and Applied Sciences aerodrome detection spatial awareness artificial intelligence deep learning machine learning |
| title | AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training |
| title_full | AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training |
| title_fullStr | AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training |
| title_full_unstemmed | AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training |
| title_short | AeroRunway: Diverse Weather and Time of Day Aerial Dataset for Autonomous Landing Training |
| title_sort | aerorunway diverse weather and time of day aerial dataset for autonomous landing training |
| topic | aerodrome detection spatial awareness artificial intelligence deep learning machine learning |
| url | https://dergipark.org.tr/en/download/article-file/4004340 |
| work_keys_str_mv | AT aliberkol aerorunwaydiverseweatherandtimeofdayaerialdatasetforautonomouslandingtraining AT nergispervanakman aerorunwaydiverseweatherandtimeofdayaerialdatasetforautonomouslandingtraining AT nesilbor aerorunwaydiverseweatherandtimeofdayaerialdatasetforautonomouslandingtraining |