UAV-Enhanced Multimodal Exposure Modeling for the Global Dynamic Exposure Model

Disaster risk is commonly represented as the interaction between hazard, exposure, and vulnerability. The accuracy of disaster risk assessments largely depends on the level of detail, diversity of attributes, and temporal dynamics represented in the exposure model. However, multimodal datasets—spanning crowd-sourced data like OpenStreetMap (OSM), official building registries, cadastral records, national statistics, AI-generated building data, and remote sensing—remain fragmented . They are heterogeneous in structures, scales, and resolutions creating challenges for seamless integration and consistent interpretation. The proposed method incorporates the high-resolution UAV mapping results into the Global Dynamic Exposure Model (GDE), leveraging diverse data sources for more robust disaster management. Unlike conventional data sources, UAV mapping technologies and derived building information can capture rapid spatial and temporal changes, significantly enhance the completeness, accuracy of multimodal exposure datasets. These benefits are most evident in a high-resolution, local-scale exposure modeling.

UAV mapping provides high-resolution orthophoto imagery and dense 3D point clouds as primary data sources. The orthophoto imagery enables the extraction of complete and accurate building footprints, which are used to improve and update existing building geometries, and identify newly constructed buildings that are absent from these sources. The 3D point clouds capture detailed building heights and geometric forms, allowing the generation of Level of Detail (LoD) 2+ 3D building models that serve as geometric enrichment for GDE. Furthermore, building attributes such as roof shape, number of stories, volume, and construction materials can be derived deterministically, rather than estimated as is commonly required in open datasets. By substantially reducing uncertainties in building asset representation, the proposed approach significantly enhances the accuracy and reliability of disaster risk assessments. The approach can further extend to post-disaster UAV surveys which allow rapid assessment of damaged areas and direct comparison with the most updated model before the disaster. Changes in height, volume, façades or roof condition can capture structural deformation and collapse indicators for loss evaluation and recovery planning.

Beyond geometries characteristics, UAV-derived orthophotos and point clouds provide detailed information on building geometry, height, roof form, and signs of recent modification, which characterize exposure-relevant attributes. For example, irregular roof shapes may indicate building extensions or mixed use, while large footprints with multiple entrances suggest functional subdivision or vertical complexity. Targeted field surveys, supported by tools such as StreetComplete, Field Tasking Manager from the Humanitarian OpenStreetMap Team (HOT), and KartaView (street-level photography), are conducted to augment these UAV-derived indicators in the datasets. The resulting semantic building information combined with the 2D and 3D geometries serve as the up-to-date representation, which is the essential core of a local digital twin. By integrating UAV-mapped builidng geometries with the on-site observations in OSM, together with the other datasets, the exposure modeling framework embeds local knowledge into building entities, establishing a full-scale and transferable workflow from data acquisition to exposure model enrichment. Case studies in Glückstadt, Germany, and Nairobi, Kenya, demonstrate its applicability for high-resolution, dynamic exposure modeling.