HAGDAVS Dataset

Detection and Semantic Segmentation of vehicles in drone aerial orthomosaics has applications in different fields like security, traffic and parking management, urban planning, logistics, and transportation, among many others. This paper presents the HAGDAVS dataset fusing RGB spectral channel and Digital Surface Model DSM for the detection and segmentation of vehicles from aerial drone images including three vehicle classes: car, motorcycle, and ghosts (motorcycle or car). We supply DSM as an additional variable to be included in deep learning and computer vision models for increasing its accuracy. RGB orthomosaic, RG-DSM fusion, and multi-label mask are provided in Tag Image File Format. Geo-located vehicle bounding boxes are provided in GeoJSON vector format. It also describes the acquisition of drone data, the derived products, and the workflow to produce the dataset. Researchers would benefit from using the proposed dataset to improve results in the case of vehicle occlusion, geo-location, and the need for cleaning ghost vehicles. As far as we know, this is the first openly available dataset for vehicle detection and segmentation, comprising RG-DSM drone data fusion, and different color masks for motorcycles, cars, and ghosts.无人机航空正射影像（orthomosaic）中的车辆检测与语义分割技术，可应用于安防、交通与停车管理、城市规划、物流以及交通运输等众多领域。本文提出了融合RGB光谱通道与数字表面模型（DSM, Digital Surface Model）的HAGDAVS数据集，用于无人机航空影像中的车辆检测与分割任务，涵盖三类车辆类别：轿车、摩托车以及虚影目标（motorcycle或car）。我们将DSM作为额外特征变量提供，可集成至深度学习与计算机视觉模型中以提升模型精度。RGB正射影像、RG-DSM融合数据以及多标签掩码均以标签图像文件格式（Tag Image File Format）提供。带有地理定位信息的车辆边界框以GeoJSON矢量格式提供。本文还阐述了无人机数据的采集流程、衍生产品的生成方法以及数据集的构建流程。研究人员可借助本数据集，针对车辆遮挡、地理定位以及虚影车辆清理等场景优化模型效果。据我们所知，本数据集是首个公开可用的融合RG-DSM无人机数据的车辆检测与分割数据集，同时为摩托车、轿车以及虚影目标提供了不同色彩的掩码标注。