Rock Slope Hazard, Vulnerability and Risk Modelling Using Remotely Sensed Data and Data-Driven Techniques

Dear Colleagues,

The advances realized over the last few decades concerning proximity remote sensing techniques and close-range terrestrial digital photogrammetry allow for the fast and direct acquisition of rock mass structural features and surface characteristics for subvertical rock surfaces. Such techniques are particularly effective in monitoring the stability of rock surfaces with limited accessibility or strongly occluded by vegetation, allowing for the collection of large data sets for predictive modelling, hazard assessments and risk analyses. Reliable generations of 3D high-resolution digital surface models (DSM) represent a crucial tool for the accurate mapping of geostructural features, identifying past events, assessing the probability of the occurrence of impending rock slope instabilities and quantifying their evolution.

This Special Issue aims to present novel contributions, case studies and applications of recent advances in remote sensing regarding studying the stability of rock slopes, their hazard and vulnerability assessments and risk management. In particular, the application of advanced machine learning models in rock slope characterisation, hazard and risk modelling are welcome topics. Examples of contributions include:

• Rock mass characterisation;
• Rock slope stability assessment;
• Developments in rock slopes monitoring equipment and fused monitoring sensing;
• Application of remote sensing data and numerical modelling for rock slope stability prediction;
• Rockfall hazard and risk modelling;
• Application of artificial intelligence approaches to analyse large monitoring data sets for rock slope hazard assessment;
• Use of remote sensing techniques for the quantification of rockfall events;
• The integration of different remote sensing techniques for rock slope stability analyses.

Prof. Dr. Anna Giacomini
Dr. Riccardo Roncella
Dr. Klaus Thoeni
Guest Editors

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