Danijel Schorlemmer
I work as a senior scientist at the German Research Centre for Geosciences in Potsdam, Germany, in the section "Earthquake Hazard and Risk Dynamics". My current main project is the first dynamic exposure model on the building scale for different natural hazards. I also work on assessments of recording quality of seismic networks, on earthquake forecasting for hazard modeling, and on testing frameworks for earthquake forecast models. I am an active OSM mapper since 2009. I have combined my enthusiasm for OSM with my scientific work, which results in the building exposure model being based on OSM data. During my scientific career, I have lived in Switzerland, USA and Japan and worked in the respective earthquake institutes of these countries.
Sessions
The substantial reduction of disaster risk and life 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, in case of natural disasters, the life of communities, represented by local governments and individuals. The framework states that communities participating in risk assessments should increase their understanding of efficient risk mitigation measures.
Earthquakes are threatening many regions in the world with constantly increasing risk due to rapid urbanization and industrialization. Earthquakes do not kill people, buildings do. Thus, the main threat of earthquakes comes from building damage and collapse. To improve resilience and preparedness, we need to estimate the risk, the possible damage of buildings and the related human and financial losses. This requires not only the position, size and class of buildings, but also the reconstruction value and the number of people inside the building at any time. For this, exposure models are used that translate the physical earthquake hazard to building damage, human and financial losses. Exposure models usually describe the built environment of administrative regions as groups (aggregates) of different building classes and their frequency.
We present our open, dynamic, and global approach to describe, model, and classify every building on Earth with the greatest level of detail. Our model is based on the building data from OpenStreetMap and engineering information from open exposure models, combining these two sources to a building-by-building description of the exposed assets. We retain the aggregated descriptions where the building coverage in OpenStreetMap is incomplete and describe every building separately where building data is available. Due to the near-real-time computations of our model, it directly profits from the growth of OpenStreetMap and with about 5 million buildings added each month (or approx. 2 per second), the areas of incomplete coverage are constantly shrinking, making way for our building-specific exposure model.
Here, we introduce shortly the earthquake phenomenon, how it affects the built environment, why a high level of detail is necessary for useful assessments of the impact and the consequences of earthquakes, how OpenStreetMap and other open data helps us to achieve this goal and how communities can benefit for the model for their own risk assessments.
OpenStreetMap (OSM) is the largest crowd-sourced mapping effort to date, with an infrastructure network that is considered near-complete. The mapping activities started as any crowd-sourced information platform: the community expanded OSM anywhere there was a collective interest. Initial efforts were found around universities or hometowns of mappers. Events, such as natural disasters can also trigger a major update. The recent earthquakes in Turkey and Syria lead to a massive contribution by the Humanitarian OSM Team (HOT) of more than 1.7 million buildings in the region in less than a month after the event1. This type of activities result in a map that is of non-uniform completeness, with some areas having all building footprints in, while other areas remain incomplete or even untouched. Currently, with 550 million footprints, OSM identifies between a quarter and half of the total building footprints in the world, if we estimate that there are around 1-2 billion buildings in the world.
A global view on the local completeness of buildings in OSM did not yet exist. Unlike other efforts, that only look at a subset of OSM building data (Biljecki & Ang 2020; Orden et al., 2020; Zhou et al., 2020), we have used the Global Human Settlement Layer (GHSL) to estimate completeness of the entire dataset. The remote sensing dataset is distributed onto a grid of approximately 100x100 meter tiles. In each tile of the grid, the built area of GHSL is compared to the total area of OSM building footprints. The computed ratio is measured against a completeness threshold that is calibrated using areas that were manually assessed.
Using information derived from remote sensing datasets can be problematic: GHSL does not only measure building footprints: it includes any human-built structures, including infrastructure and industrial areas. Next to that, due to sub-optimal input data or failing algorithms, the dataset is not of the same quality as the crowd-sourced data in OSM in areas that are complete. Even with these limitations, a comprehensive global completeness assessment is created. The assessment should not be used as ground truth, but rather as reflection on the OSM building dataset as is and as a guideline for priorities for the future. Statistics on regional completeness can be created and the quality of GHSL could be assessed on countries that are considered to be complete, such as France or the Netherlands.