EGU2020-18920
https://doi.org/10.5194/egusphere-egu2020-18920
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Global Dynamic Exposure and the OpenBuildingMap - A Big-Data and Crowd-Sourcing Approach to Exposure Modeling

Danijel Schorlemmer1, Thomas Beutin1, Fabrice Cotton1, Nicolas Garcia Ospina1, Naoshi Hirata2, Kuo-Fong Ma3, Cecilia Nievas1, Karsten Prehn1, and Max Wyss4
Danijel Schorlemmer et al.
  • 1GFZ German Research Centre for Geosciences, Potsdam, Germany (ds@gfz-potsdam.de)
  • 2National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan
  • 3Earthquake - Disaster & Risk Evaluation and Management (E-DREaM) center, National Central University, Taiwan
  • 4International Centre for Earth Simulation, Geneva, Switzerland

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.

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. 

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.

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. 

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.

How to cite: Schorlemmer, D., Beutin, T., Cotton, F., Garcia Ospina, N., Hirata, N., Ma, K.-F., Nievas, C., Prehn, K., and Wyss, M.: Global Dynamic Exposure and the OpenBuildingMap - A Big-Data and Crowd-Sourcing Approach to Exposure Modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18920, https://doi.org/10.5194/egusphere-egu2020-18920, 2020

Comments on the presentation

AC: Author Comment | CC: Community Comment | Report abuse

Presentation version 2 – uploaded on 04 May 2020 , no comments
Presentation version 1 – uploaded on 04 May 2020 , no comments