E3S Web Conf.
Volume 7, 20163rd European Conference on Flood Risk Management (FLOODrisk 2016)
|Number of page(s)||13|
|Section||Physical, economic and environmental consequences|
|Published online||20 October 2016|
Application of an Earth-Observation-based building exposure mapping tool for flood damage assessment
1 Institute for Advanced Study of Pavia, Palazzo del Broletto, Piazza della Vittoria, 15, I-27100 Pavia, Italy
2 CIMA Research Foundation, Via Armando Magliotto, 2, I-17100 Savona, Italy
3 Dept. ECBE, University of Pavia, Via Ferrata, 1, I-27100 Pavia, Italy
a Corresponding author: firstname.lastname@example.org
Detection and characterization of territorial elements exposed to flood is a key component for flood risk analysis. Land-use description works well for small scales of representation but it becomes too coarse while increasing the scale. “Single-element” characterization is usually achieved through surveys, which become prohibitive as the amount of elements to be characterized increases. Mapping schemes represent a compromise between level of description and efforts for data collection. The basic idea is to determine the statistical distribution of building characteristics inside a homogeneous class starting from a sample area and to apply this distribution to the whole area, realizing a statistical extrapolation. An innovative approach was developed, merging the mapping scheme methodologies developed by the Global Earthquake Model  and Blanco–Vogt and Schanze , in which homogeneous classes are not development areas but building clusters. The approach was applied to the buildings in the Bisagno River floodplain, Genoa (Italy). Buildings were classified according to a building taxonomy. Once the percentage of basement presence was assigned to each class by surveying a limited subset of the exposed assets, a series of possible basement distributions was simulated to calculate the corresponding damage distributions for a real flood event. The total average damage obtained is very close to the refund claims, with a percentage error lower than 2%.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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