mlsanityData to 14 Oct 2026

AirBirds: A Large-scale Challenging Dataset for Bird Strike Prevention in Real-world Airports

arXiv 2304.11662 ↗First posted 23 Apr 2023cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

One fundamental limitation to the research of bird strike prevention is the lack of a large-scale dataset taken directly from real-world airports. Existing relevant datasets are either small in size or not dedicated for this purpose. To advance the research and practical solutions for bird strike prevention, in this paper, we present a large-scale challenging dataset AirBirds that consists of 118,312 time-series images, where a total of 409,967 bounding boxes of flying birds are manually, carefully annotated. The average size of all annotated instances is smaller than 10 pixels in 1920x1080 images. Images in the dataset are captured over 4 seasons of a whole year by a network of cameras deployed at a real-world airport, covering diverse bird species, lighting conditions and 13 meteorological scenarios. To the best of our knowledge, it is the first large-scale image dataset that directly collects flying birds in real-world airports for bird strike prevention. This dataset is publicly available at https://airbirdsdata.github.io/.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 23 Apr 2023.

Builds on

22 of 33 references.

References · 22

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10 more
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Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2023MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Resultscited by 8
  2. 2024FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Videocited by 5
  3. 2025MVA 2025 Small Multi-Object Tracking for Spotting Birds Challenge: Dataset, Methods, and Resultscited by 2
  4. 2025Not All Birds Look The Same: Identity-Preserving Generation For Birdscited by 0