[S23-1] Geoscience and society I

[S23-1-02] Global Dynamic Exposure and the OpenBuildingMap - Communicating Risk and Involving Communities

^○Danijel Schorlemmer^{1, 4}, Thomas Beutin¹, Naoshi Hirata², Max Wyss³, Fabrice Cotton¹, Karsten Prehn¹ (1.GFZ German Research Center for Geosciences, Potsdam, Germany, 2.Earthquake Research Institute, University of Tokyo, Japan, 3.International Centre for Earth Simulation Foundation, Geneva, Switzerland, 4.Universiy of Southern California, Los Angeles, USA)

The substantial reduction of disaster risk and live losses, 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 it affects the life of communities in case of natural disasters. These dynamics are best understood and captured by local communities, following two of the guiding principles UNISDR formulated: "empowerment of local authorities and communities" and "engagement from all of society". Moreover, communities that participate in risk assessments increase their understanding of efficient risk mitigation measures. Our Global Dynamic Exposure model and its technical infrastructure build on the involvement of communities in a citizen-science approach. Simultaneously, it helps educating community members in the risks they are facing and how they can prevent losses of lives.

We are employing a crowd-sourced exposure capturing using OpenStreetMap (OSM), an ideal foundation with already more than 200 million building footprints (growing by ~100'000 per day), and a plethora of information about school, hospital, and other critical facilities. Besides relying on the very active OSM community, we are providing a tailored building capture tool for mobile devices, facilitating simple and fast building property capturing for OSM. With our OpenBuildingMap system, we are harvesting OSM by processing every building in near-realtime. We are collecting exposure and vulnerability indicators from explicit data (e.g. hospital locations), implicit data (e.g. building shapes), and semantically derived data, i.e. interpretation applying expert knowledge, and translate them into building classifications (e.g. EMS98).

With our tools, interested communities can capture their exposure and analyze how natural disaster will affect them. It helps communicating risks down to the community level and provides the means to involve communities to mitigate risks.