mlsanityData to 7 Oct 2026

NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles

arXiv 2302.09429 ↗First posted 18 Feb 2023cs.CVcited by 2

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Abstract (arXiv)

This paper presents an open-source aerial neuromorphic dataset that captures pedestrians and vehicles moving in an urban environment. The dataset, titled NU-AIR, features 70.75 minutes of event footage acquired with a 640 x 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment. Crowds of pedestrians, different types of vehicles, and street scenes featuring busy urban environments are captured at different elevations and illumination conditions. Manual bounding box annotations of vehicles and pedestrians contained in the recordings are provided at a frequency of 30 Hz, yielding 93,204 labels in total. Evaluation of the dataset's fidelity is performed through comprehensive ablation study for three Spiking Neural Networks (SNNs) and training ten Deep Neural Networks (DNNs) to validate the quality and reliability of both the dataset and corresponding annotations. All data and Python code to voxelize the data and subsequently train SNNs/DNNs has been open-sourced.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 45 of its 123 references that are in the map. First posted on arXiv 18 Feb 2023.

Builds on

45 of 123 references.

References · 45

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perceptioncited by 1
  2. 2026Neuromorphic Object Detection: An In-Depth Study and Future Directionscited by 0