<p>The substantial reduction of disaster risk and loss of life, a major goal of the Sendai Framework by the United Nations Office for Disaster Risk Reduction (UNISDR), requires a clear understanding of the dynamics of the built environment and how they affect, in the case of natural disasters, the life of communities, represented by local governments and individuals. These dynamics can be best understood and captured by the local communities themselves, following two of the guiding principles formulated by the UNISDR: "empowerment of local authorities and communities" and "engagement from all of society". The two lead to societies increasing their understanding of efficient risk mitigation measures.</p><p>Our Global Dynamic Exposure model and its technical infrastructure build on the involvement of communities in a citizen-science approach. We are employing a crowd-sourced exposure capturing based on OpenStreetMap (OSM), an ideal foundation with already more than 375 million building footprints (growing daily by ~150,000), and a plethora of information about school, hospital, and other critical facilities. We are harvesting this dataset with our OpenBuildingMap system by processing the information associated with every building in near-real-time. We are enriching this dataset in a truly big-data approach by including built-up area detection from remote sensing with satellite and radar imagery combined with different sources of road networks, as well as various open datasets and aggregated exposure models that provide relevant additional information on, buildings and land use.&#160;</p><p>A task of such a scale does not come without challenges, particularly in matters of data completeness, privacy and the merging and homogenizing of different datasets. We are thus investing a large effort on the development of strategies to tackle these in a transparent and consistent way.</p><p>We are fully automatically collecting exposure and vulnerability indicators from explicitly provided data (e.g., hospital locations), implicitly provided data (e.g., building shapes and positions), and semantically derived data, that is, interpretation applying expert knowledge. The latter allows for the translation of simple building properties as captured by OpenStreetMap users or taken from open datasets into vulnerability and exposure indicators and subsequently into building classifications as defined in the Building Taxonomy 2.0 developed by the Global Earthquake Model (GEM) and in the European Macroseismic Scale (EMS98). A task of such a scale does not come without challenges, particularly in matters of data completeness, privacy and the merging and homogenizing of different datasets. We are thus investing a large effort on the development of strategies to tackle these in a transparent and consistent way. With our open approach, we increase the resolution of existing exposure models minute by minute through data updates and step by step with each added building, as we move forward from aggregated to building-by-building descriptions of exposure.&#160;</p><p>We expect the quality of near-real-time estimates of the extent of natural disasters to increase by an order of magnitude, based on the data we are collecting. We envision authorities and first responders greatly benefitting form maps pinpointing the greatest trouble spots in disasters and from detailed quantitative estimates of the likely damage and human losses.</p>
The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.