38802462-2f56-422b-b537-ef212c89141420210422022526551wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT2224-34961790-507910.37394/232015http://wseas.org/wseas/cms.action?id=40311820211820211710.37394/232015.2021.17https://wseas.org/cms.action?id=23258ErnestoBernardoDiceam – Civil, Energy Environmental and Material Engineering Department Mediterranea University Località Feo Di Vito, 89124 Reggio Calabria, ItalyStefanoBonfaUk Economic Interest Grouping (Ukeig) N. Gee000176, 87 Hungerdown E4 6qj London, United KingdomSalvatoreCalcagnoDiceam – Civil, Energy Environmental and Material Engineering Department Mediterranea University Località Feo Di Vito, 89124 Reggio Calabria, ItalyThe 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.42220214222021371385https://www.wseas.org/multimedia/journals/environment/2021/a745115-626.pdf10.37394/232015.2021.17.37https://www.wseas.org/multimedia/journals/environment/2021/a745115-626.pdf10.1109/morse48060.2019.8998713S. 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 10.3390/rs8090786T. N. Tonkin and N. G. 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