A novel model for multi-risk ranking of buildings at city level based on open data: the test site of Rome, Italy

Abstract

In the context of population concentration in large cities, assessing the risks posed by geological hazards to enhance urban resilience is becoming increasingly important. This study introduces a robust and replicable procedure for assessing ground instability hazards and associated physical risks. Specifically, our comprehensive model integrates spatial hazard assessments, multi-satellite InSAR data, and physical features of the built environment to rank and prioritize assets facing multiple risks, with a focus on ground instabilities. The model generates risk scores based on hazard probability, potential damage, and displacement rates, aiding decision-makers in identifying high-risk buildings and implementing appropriate mitigation measures to reduce economic losses. The procedure was tested in Rome, Italy, where the analysis revealed that 60% of the examined buildings (90 × 10³) are at risk of ground instability. Specifically, 33%, 22%, and 5% exhibit the highest multi-risk score for sinkholes, landslides, and subsidence, respectively. Landslide risk prevails among residential structures, while retail and office buildings face a higher risk of subsidence and sinkholes. Notably, our study identified a positive correlation between mitigation expenses and the multi-risk scores of nearby buildings, highlighting the practical implications of our findings for urban planning and risk management strategies.

Keywords:

• Multi-risk
• ground instability hazards
• urban resilience
• data fusion
• InSAR
• open data

Acknowledgements

The authors are grateful to the Italian Space Agency (ASI) for providing the Cosmo-SkyMed SAR images free of charge to the CERI research centre for academic research purposes (project card ID: 615).

Authors contributions

G.M., C.E., and P.M. conceptualized and coordinated the research activities. G.M. gathered the data. G.M., C.M., and R.M. analysed and processed the interferometric data. G.M. wrote the code and performed the multi-risk analysis; G.M. prepared the figures and wrote the manuscript. C.E., P.M., and G.S.M. supervised the project. G.S.M. provided research grants as the principal investigator of the acknowledged project. All authors reviewed the final version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All input data and the code are accessible online at the author’s GitHub page (https://github.com/gmastrantoni/mhr).

Additional information

Funding

This research is framed within the project ‘Geo-multi-hazard analysis through geospatial data driven methods for resilient cities’ funded by Sapienza University of Rome. Project protocol number: RM12117A86DC0211.