HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics

John R. Ballesteros, German Sanchez-Torres and John W. Branch-Bedoya
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John R. Ballesteros: Facultad de Minas, Universidad Nacional de Colombia, Medellín 050041, Colombia
German Sanchez-Torres: Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470001, Colombia
John W. Branch-Bedoya: Facultad de Minas, Universidad Nacional de Colombia, Medellín 050041, Colombia

Data, 2022, vol. 7, issue 4, 1-14

Abstract: Detection and Semantic Segmentation of vehicles in drone aerial orthomosaics has applications in a variety of fields such as 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: cars, motorcycles, and ghosts (motorcycle or car). We supply DSM as an additional variable to be included in deep learning and computer vision models to increase 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. We 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.

Keywords: vehicle detection; semantic segmentation; orthomosaics; Geographic Information Systems (GIS) (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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