本文提出了一个开源的航空神经形态数据集，用于捕捉城市环境中移动的行人和车辆。该数据集名为NU-AIR，包含70.75分钟的事件录像，由安装在四旋翼飞行器上的640 x 480分辨率神经形态传感器在城市环境中采集。数据集中记录了不同高度和光照条件下的人群、不同类型的车辆以及繁忙的城市街景。录像中的车辆和行人以30 Hz的频率提供手动边界框标注，共产生93,204个标签。通过三个脉冲神经网络（SNNs）的综合消融研究以及训练十个深度神经网络（DNNs）来评估数据集的保真度，以验证数据集及相应标注的质量和可靠性。所有数据以及用于体素化数据并随后训练SNNs/DNNs的Python代码均已开源。
英文摘要：
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.
Beyond Appearance: A Multi-cue Framework and Large-scale Benchmark for Pedestrian Association and Tracking on Mobile Aerial-Ground Platforms
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机科学学院）；Ningbo Institute, Northwestern Polytechnical University（西北工业大学宁波研究院）；National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（国家空天地海一体化大数据应用技术工程实验室）；Shenzhen University of Advanced Technology（深圳先进技术研究院）
Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases
Australian Centre for Field Robotics, University of Sydney（悉尼大学澳大利亚野外机器人中心）；University Institute for Computer Research, Universidad de Alicante（阿利坎特大学计算机研究所）
The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices
Instituto de Telecomunicações（电信研究所）；University of Beira Interior（贝拉内格罗大学）；India Statistical Institute（印度统计研究所）；JSS Science and Technology University（JSS科技大学）
UAV-Track VLA: Embodied Aerial Tracking via Vision-Language-Action Models
Beijing Institute of Technology（北京理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Sanya（三亚学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Hunan University（湖南大学）；Beihang University（北京航空航天大学）；Flying Intelligence Team (Virtual Research Community)（飞行智能团队（虚拟研究社区））
Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；First Surveying and Mapping Institute of Hunan Province（湖南省第一测绘院）
A Data-Driven RetinaNet Model for Small Object Detection in Aerial Images
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；University of Missouri（密苏里大学）；Computer Science University of Missouri Columbia, MO, USA（计算机科学密苏里大学哥伦比亚分校）
A-PAIR: A Benchmark and Identity-Consistent Grounding Framework for Air-Ground Cross-View Referring Person Detection
School of Automation, Southeast University（东南大学自动化学院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Sports Training, Tianjin University of Sport（天津体育学院运动训练学院）；Tianjin University（天津大学）；National University of Defense Technology（国防科技大学）
Autonomous navigation for low-altitude UAVs in urban areas
Survey Copter / Airbus Defense and Space（空客防务与航天公司Survey Copter部门）；Hubert Curien Laboratory（于贝尔·居里安实验室）；Center for Research in Computer Vision, UCF（中佛罗里达大学计算机视觉研究中心）
