Simplifying Mapping for Building Exposure using OpenStreetMap Tools

Geospatial data is essential for disaster risk assessment but it is often fragmented across independent taxonomies such as PAGER, GEM, OASIS, and the Building Stock Observatory. Each of these frameworks provides structured and detailed information, yet differences in schemas and terminology limit integration and broader reuse. OpenStreetMap (OSM), as a widely adopted open geospatial platform, offers a practical baseline for integration. Its surrounding ecosystem includes a rich set of tools that are critical for humanitarian mapping such as JOSM and Tasking Manager that can integrate the exposure-related features.

Aligning diverse building taxonomies with OSM enables structured datasets to be compared and cross-referenced within a common framework, but it also requires balancing different levels of detail. Not all information used in exposure or risk modeling is useful for the mapping community, as they concentrate on visible features. Attributes such as population or structural value are critical for exposure analysis, but often are based on estimates derived from regional statistics and based not on mapping in the ground. So, we use the OSM tools and tagging standards to provide the semantic backbone, while exposure-related information is integrated through controlled, range-based tags that remain compatible with OSM practices and reflect inherent uncertainty.

This is done through tagging presets that are defined for both physical building characteristics and exposure-related attributes. Observable features such as material, height class, and occupancy follow established OSM conventions, while complementary exposure presets allow contributors to assign population and structural value ranges based on reference values from the Global Dynamic Exposure project. These exposure-relevant values provide a consistent starting point but can be refined using local statistics or expert judgment. For example, a residential masonry building mapped in OSM can be tagged with its material and height class, and additionally assigned a population range and a structural value class derived from regional reference estimates. The OSM-relevant information is pushed to the open dataset, and the refined exposure information can be used to estimate risk or damage in a specific area.

By embedding this workflow into existing OSM editors, humanitarian organizantions and institutions can use familiar tools to efficiently map areas and characterize exposure, improving data consistency and supporting disaster risk assessment, humanitarian response, and resilience planning.