“PROION” is a research project, focused on the development of a platform for the continuous monitoring of high priority infrastructure (public infrastructure, dams, bridges, etc.) in the broader area of the Hellenic Supersite, named Enceladus. The project started on September 2020 and it was financially supported by the European Union and the Hellenic government. Monitoring is based on the combination of instrumental and remote sensing measurements in conjunction with soft computing algorithms to assess infrastructure stability. The results of the project are presented in the current study.
«Acknowledgment: This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EΔK-02396 Μultiparametric monitoring platform with micro-sensors of eNceladus hellenIc supersite)».
Climate change constitutes a serious global challenge with consequences that are directly affecting infrastructure. Thus, there is a great need to develop reliable cost-effective systems, which integrate remote sensing data, in situ measurements and advanced methods for infrastructure monitoring. In this framework, the European Union and the Hellenic government are financially supporting a R&D project, named “PROIΟΝ”. The purpose of the project is the development of a platform for the continuous monitoring of high-priority infrastructures (public infrastructure, dams, bridges, etc.) which are located in a particularly active area in terms of tectonics and seismicity. Monitoring is based on the combination of instrumental and remote sensing measurements along with fuzzy logic networks methods and machine learning algorithms in order to generate an innovative decision-making and decision-support tool. Specifically, measurements derived from three-axis accelerometers, Global Navigation Satellite System (GNSS) receivers and Persistent Scatterer Interferometry are imported into the platform. The measurements will be validated using high-accuracy reference representations arising from data acquired by Terrestrial Laser Scanning (TLS) surveys and Unmanned Aerial Vehicles (UAV) campaigns and subsequently, deformation maps will be generated. Intelligent data analysis methods will contribute to making decisions on the current as well as the future state of the infrastructure. At this initial stage of the project, the proposed monitoring system is described in detail.
Mass movements and therefore rockfalls are common natural hazards that require the development of new methodologies and techniques for an effective research, which could help governments, communities or policy makers in the adoption of the most appropriate practices. In that context different remote sensing data have been used and several methodologies have been tested. In the present study, we initially map a rockfall occurred on the settlement Myloi, which is located near the village of Andritsaina in Western Greece, while later we estimated the volume of rock fragments. The data sets consist of repeated GNSS measurements, laser scanning surveys and UAV campaigns over the study area. The precise mapping of the rockfall was carried out through the processing of GNSS measurements. However, mapping was also performed using orthophotos derived from UAV data and 3D images of laser scanning campaigns. Regarding the volume estimation, three methodologies were applied -two of them were photogrammetric and one was geophysical- using ArcGIS, Cloud Compare and Oasis Montaj from Geosoft respectively. The selection of different types of data and processing methodologies took place within the framework of the comparison of their results in terms of accuracy as well as the achievement of their synergy in the direction of a more detailed research.
Both Terrestrial Laser Scanner and Structure from Motion photogrammetry are considered as reliable methods for collecting relief elevation data in the form of dense point clouds. The point density is their common big advantage compared to classical survey measurements. Although both methods produce millions of XYZ points, the manner of collecting data is different resulting in point clouds with serious differences. In the current study these two methodologies for 3D point cloud generation are compared. As a test site is considered a part of a well mapped active landslide in western Greece. In this landslide a terrestrial laser scanner was deployed for the collection of 3D information of the steep slopes. At the same time UAVs flights were performed and dense point clouds were also generated. The derived products from the TLS and UAV are compared and the results are presented.
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