Affiliation:
1. Diceam – Civil, Energy Environmental and Material Engineering Department Mediterranea University Località Feo Di Vito, 89124 Reggio Calabria, Italy
2. Uk Economic Interest Grouping (Ukeig) N. Gee000176, 87 Hungerdown E4 6qj London, United Kingdom
Abstract
The proposed research activity is based on the study and development of advanced survey and monitoring techniques for the control and mapping of road infrastructures. Specifically, we want to create an automated monitoring system mainly through the use of drones that at pre-established time steps acquire the data necessary for the continuous monitoring of the functional characteristics of the road infrastructure and the public usability of dynamic data. Subsequently, through the implementation of algorithms dedicated to the management of the amount of georeferenced data acquired - big data - the same will be represented on GIS (Geographic Information System) platforms as "open and updatable" thematic cartography, which can be integrated with further data collected both with of traditional Geomatics (GNSS receivers, motorized total station and 3D laser scanner) and innovative ones (remote sensing, Mobile Mapping Systems (road vehicles and UAVs)). This context also includes the establishment and updating of the Road Cadastre, introduced by the Ministerial Decree of 01/06/2001 No. 6, intended as an IT tool for archiving, viewing, querying and managing all the data that the body owner / manager owns on its own road network.
Publisher
World Scientific and Engineering Academy and Society (WSEAS)
Subject
General Energy,General Environmental Science,Geography, Planning and Development
Reference34 articles.
1. S. Ogawa, K. Matsushima and O. Takahashi, Efficient Pavement Crack Area Classification Using Gaussian Mixture Model Based Features, International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE), Bali, Indonesia, 2019, pp. 75-80. Doi: 10.1109/MoRSE48060.2019.8998713
2. T. N. Tonkin and N. G. Midgley, GroundControl Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry, Remote Sens., Vol.8, Issue 9, 2016, pp. 786. Doi: https://doi.org/10.3390/rs8090786
3. B. Sekeroglu, K. Tuncal, Image Processing in Unmanned Aerial Vehicles, Al-Turjman F. (eds) Unmanned Aerial Vehicles in Smart Cities. Unmanned System Technologies, Springer, Cham, 2020, pp. 167-179. Doi: https://doi.org/10.1007/978-3-030-38712- 9_10
4. F. Dadrasjavan, N. Zarrinpanjeh, A. Ameri, Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery, Preprints, 2019, 2019070009. Doi: 10.20944/preprints201907.0009.v1
5. S. Chen, L. Truong-Hong, D. F. Laefer, E. Mangina, (2018), Automated Bridge Deck Evaluation through UAV Derived Point Cloud, CERI-ITRN2018, Dublin, Ireland, 2018, pp. 735-740.
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