High-resolution exposure models for coastal cities in Northern Adriatic for multi-risk analysis

Multi-hazard disaster risk reduction and mitigation require high-resolution exposure models that grasp the characteristics of assets at the local scale. High-resolution exposure models may allow improving precision/accuracy of risk and damage assessments, especially for hazards which are characterised by high spatial variability or may be influenced by the presence of the assets, such as tsunami or flooding. We propose a methodology for developing a high-resolution population and residential buildings exposure models, to be used for multi-hazard risk reduction purposes at the local scale.  This method has been tested and validated for a selected coastal area in the upper Adriatic, exposed to multiple hazards including earthquakes, tsunamis, meteorological events and coastal erosion. For the development of the population exposure model, a high-resolution population density data, collected at global scale, is combined with the national population census data, leveraging  both on the accuracy of the national census and on the resolution of the global data. Also, the building census data is complemented with exposure indicators extracted from digital building footprints from the Carta Tecnica Regionale Numerica (CTRN),  which is missing in census data, such as average built area, total built area, replacement cost, height and plan regularity. The final exposure layers are assembled at two resolutions: 100 meters and 30 meters, with information also provided at the census unit level. We discuss the development and use of these layers for multi-risk assessment and their potential combination with artificial intelligence. 

This research is a contribution to the projects: RETURN Extended Partnership (European Union Next-Generation EU—National Recovery and Resilience Plan—NRRP, Mission 4, Component 2, Investment 1.3—D.D. 1243 2/8/2022, PE0000005); PRIN-PNRR project SMILE: Statistical Machine Learning for Exposure development, funded by the European Union- Next Generation EU, Mission 4 Component 1 (CUP F53D23010780001).