High-resolution multi-hazard residential buildings and population exposure model for coastal areas: A case study in northeastern Italy

Developing high-resolution multi-hazard exposure models significantly improves risk assessment and loss estimation. In the present study, we propose and verify a methodology for developing a high-resolution exposure model for population and residential buildings that could be used for multi-hazard risk mitigation at the local scale across the globe. The methodology is applied to Lignano municipality, a coastal area located in the northern Adriatic, prone to multiple hazards such as seismically-induced tsunamis, meteorological events, coastal erosion and subsidence. The population exposure layer is developed integrating population data with demographic characteristics and socio-economic indicators. In parallel, the building exposure layer, which combines census data with digital building footprints, contains information about: geographic distribution, age of construction or retrofit, number of storeys, construction material types, average built-up area, structural replacement cost, and structural regularity. These data layers are made available at two resolutions: 100 m and 30 m, with information also provided at the census unit level. We describe the methodology developed for exposure assessment and discuss its potential use for multi-hazard risk assessment in coastal areas. • Developing a high-resolution exposure model for multi-risk reduction purposes. • Population exposure model is developed combing Meta data with census population. • Population is classified into many classes based on its demographic characteristics. • Building exposure model combines census buildings with digital building footprints. • These models are available at the census unit level, 100m and 30m resolutions.

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Publication Details

Journal
International Journal of Disaster Risk Reduction
Published
2025-03-18
DOI
https://doi.org/10.1016/j.ijdrr.2025.105403
Citations
16
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
17.69
Controls
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article

High-resolution multi-hazard residential buildings and population exposure model for coastal areas: A case study in northeastern Italy

Hazem Badreldin, Antonella Peresan, Chiara Scaini, Hany M. Hassan
16 citations
International Journal of Disaster Risk Reduction
Flood Risk Assessment and Management
17.69
article

High-resolution multi-hazard residential buildings and population exposure model for coastal areas: A case study in northeastern Italy

Hazem Badreldin, Antonella Peresan, Chiara Scaini, Hany M. Hassan
article en
16 citations

Abstract

Developing high-resolution multi-hazard exposure models significantly improves risk assessment and loss estimation. In the present study, we propose and verify a methodology for developing a high-resolution exposure model for population and residential buildings that could be used for multi-hazard risk mitigation at the local scale across the globe. The methodology is applied to Lignano municipality, a coastal area located in the northern Adriatic, prone to multiple hazards such as seismically-induced tsunamis, meteorological events, coastal erosion and subsidence. The population exposure layer is developed integrating population data with demographic characteristics and socio-economic indicators. In parallel, the building exposure layer, which combines census data with digital building footprints, contains information about: geographic distribution, age of construction or retrofit, number of storeys, construction material types, average built-up area, structural replacement cost, and structural regularity. These data layers are made available at two resolutions: 100 m and 30 m, with information also provided at the census unit level. We describe the methodology developed for exposure assessment and discuss its potential use for multi-hazard risk assessment in coastal areas. • Developing a high-resolution exposure model for multi-risk reduction purposes. • Population exposure model is developed combing Meta data with census population. • Population is classified into many classes based on its demographic characteristics. • Building exposure model combines census buildings with digital building footprints. • These models are available at the census unit level, 100m and 30m resolutions.

International Journal of Disaster Risk ReductionVol. 121
National Research Institute of Astronomy and Geophysics (EG), National Institute of Oceanography and Applied Geophysics (IT)
Sustainable cities and communities
Openalex Percentile: Top 1%
Flood Risk Assessment and Management
17.69
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