Exposure, i.e. the situation of people, infrastructure, housing, production capacities, and other tangible human assets located in hazard-prone areas, is one of the main components for risk assessment. While exposure at the country scale is often well-defined, fine-grained exposure datasets are key to making risk assessments more detailed and reliable, both in terms of spatial and temporal resolutions, and in identifying which people and assets are most at risk.

Models, input, and output datasets range from geospatial representations of population or built-up area to complex datasets describing people’s characteristics (e.g., gender, age, education) and detailed asset information (e.g., building material, number of floors, road types). Models cover a wide range of scales and resolutions, with local implementations closely grounded in field observations and providing high-resolution detail, while others cover continents or even have a global reach. Other approaches are designed around specific sectors or economic activities, such as agriculture, tourism, or embodied carbon accounting. Various data sources range from census data, cadastral surveys, and grassroots activities, including OpenStreetMap-based exposure models, to big data generated through remote sensing and AI-driven methods, often developed by larger organisations (e.g., WorldPop, Open Buildings by Google, or Overture Maps). Recent efforts increasingly move beyond static representations of exposure to capture its dynamics, including the modelling of Shared Socioeconomic Pathways and human displacement. This broad and growing landscape of exposure science is reflected in the wide variety of stakeholders, ranging from the insurance industry to local and national governments, research organizations, the tourism sector, and NGOs.

In this session, we will welcome submissions addressing (1) geospatial methods and tools for the creation of exposure models, such as Volunteered Geographic Information (VGI) or Earth Observation (EO) and AI models; (2) assessment of the quality or completeness of the data sources of exposure models, such as remote sensing, crowd-sourced, or official registry datasets; (3) exposure models for single hazards, for multi-hazard or hazard-independent contexts; (4) comparison, validation, and analysis of exposure models; (5) Cat model, insurance, government, and financial exposure models and datasets; and (6) innovative applications of exposure models.