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ID.56796: Integration of multi-source Remote Sensing Data to detect and monitoring large and rapid landslides and use of Artificial Intelligence for Cultural Heritage preservation

Solid Earth


Remote sensing (RS) data is successfully applied since decades for the identification and monitoring of landslide phenomena at different spatio-temporal scales. However, limitations associated with data availability/accessibility (spatial coverage, low temporal revisit time, high costs) might hampered the development of operational tools.The results and analyses retrieved in the framework of D4 32365 have shown the great benefits of RS in monitoring multi-hazards. The wide spatial and temporal data availability allowed a detailed description of landslide histories even of remote regions. Therefore, the continuous monitoring of such hazards, namely large landslides, is of fundamental importance to minimize and prevent the actual and future risks. In this D5 proposal, we foresee to continue the monitoring activities started with the D4 project mainly by means of multi-source RS data at diverse areas located in different countries.Our D5 proposal would also consider monitoring structures of great heritage and historical value, more quickly and effectively, as these structures are continuously subject to deformations caused by internal and external factors, especially when located in high risk areas. The availability of SAR data with spatial and temporal resolutions at an unprecedented level, associated with the new methods of SAR time series processing, allow us to think in the development of active systems for structural risks detecting and alerting. However, only the use of Artificial Intelligence techniques will allow to deal with the huge amount of data that will be generated. The Vilari├ºa Valley, located in the north of Portugal is crossed by an active fault and will be used as test site to develop the AI-based risk alert system. In this region there is a high number of buildings with historical and patrimonial interests that may be at risk. In order to cover most situations, the following Chinese sites will be also included: (1) Hani Rice Terraces; (2) Fushun West Open pit Coal Mine and (3) Shuping-Fanjiaping. Besides, the Central Karakoram Range in the Northern Pakistan, exposed to a variety of natural hazards including devastating landslides, would be an important study area on the Belt and Road.Based on the above selected areas of investigation and their multi-hazard exposure, we refer to the ÔÇÿAcross topicsÔÇÖ option. We choose the topics ÔÇÿ1. Solid EarthÔÇÖ and ÔÇÖ10. Data AnalysisÔÇÖ, with the following sub-topics:ÔÇó 1.2 – ÔÇ£Monitoring of surface deformation of large landslidesÔÇØÔÇó 1.3 – ÔÇ£Infrastructure health diagnosis and safety monitoringÔÇØÔÇó 10.2 – ÔÇ£Artificial Intelligence and Machine LearningÔÇØMain goals:- detect and analyze recent rapid landslide events with satellite and ground based EO data- evaluate the stability of landslides, forecast when and how hazards might happen in future, generate future hazard scenarios and provide support for disaster prevention and damage reduction to authorities- use latest EO technologies for monitoring historical structures leading to the early detection of potential risks and thus making it possible to increase security and significantly reduce maintenance costs- develop an AI system to process and analyze huge amount of dataThe methodology to achieve the previous objectives is the collaborative analysis of multi-source EO data, by means of satellite SAR and InSAR time-series, S2 optical images, VHR and stereo optical images, GBSAR data, GNSS and CR measurements, geological and environmental data, and data modeling.The proposed research also foresees the exchange of YS in the scope of projects 41941017 and 41877522 funded by National Natural Science Foundation of China, 07-Y30B03-9001-19/21 funded by State Administration of Science, Technology and Industry for National Defense of China´╝îDD20190514 funded by China Geological Survey, and 1/SAMA/2020/2019 (POCI-62-2019-04) funded by AMA. We also apply for funding for the YS within this project and national agencies


PI Europe
Prof. Joaquim Sousa, University of Trás-os-Montes and Alto Douro, UTAD, PORTUGAL
Co-PIs Europe
Dr. Giovanni Cuozzo, EURAC, ITALY
Dr. Ruth Sonnenschein, EURAC, ITALY
Dr. Stefan Steger, EURAC, ITALY
Dr. Perski Zbigniew, Polish Geological Institute, POLAND
Prof. Luís Reis, FEUP, PORTUGAL
PI China
Prof.. Fan Jinghui, China Aero Geophysical Survey & Remote Sensing Center for Natural Resources, CHINA
Co-PIs China
Prof. Guang Liu, RADI, CHINA
Prof. Shibiao Bai, NNU, CHINA
Prof. Pengfei Tu, CTGU, CHINA
Prof. Shiyong Yan, CUMT, CHINA
Prof. Honglei Yang, CUGB, CHINA