Next Article in Journal
Measurements of User and Sensor Data from the Internet of Things (IoT) Devices
Previous Article in Journal
The Missing Case of Disinformation from the Cybersecurity Risk Continuum: A Comparative Assessment of Disinformation with Other Cyber Threats
Data Descriptor

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

1
Facultad de Minas, Universidad Nacional de Colombia, Medellín 050041, Colombia
2
Facultad de Ingeniería, Universidad del Magdalena, Santa Marta 470001, Colombia
*
Author to whom correspondence should be addressed.
Academic Editor: Jamal Jokar Arsanjani
Received: 9 March 2022 / Revised: 9 April 2022 / Accepted: 11 April 2022 / Published: 14 April 2022
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. View Full-Text
Keywords: vehicle detection; semantic segmentation; orthomosaics; Geographic Information Systems (GIS) vehicle detection; semantic segmentation; orthomosaics; Geographic Information Systems (GIS)
Show Figures

Figure 1

MDPI and ACS Style

Ballesteros, J.R.; Sanchez-Torres, G.; Branch-Bedoya, J.W. HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics. Data 2022, 7, 50. https://doi.org/10.3390/data7040050

AMA Style

Ballesteros JR, Sanchez-Torres G, Branch-Bedoya JW. HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics. Data. 2022; 7(4):50. https://doi.org/10.3390/data7040050

Chicago/Turabian Style

Ballesteros, John R., German Sanchez-Torres, and John W. Branch-Bedoya. 2022. "HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics" Data 7, no. 4: 50. https://doi.org/10.3390/data7040050

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop