Exploring the relation of livability mapping and flood exposure analysis by combining remote sensing and citizen science

Environmental hazards are key determinants of urban liveability, shaping the safety, health, and resilience of residents. This study investigates the intersection of urban livability and flood exposure by integrating remote sensing, citizen science, and AI-driven analysis across three African countries: Ghana, Kenya, and Mozambique. Using Sentinel-1 satellite imagery, open geospatial datasets, and advanced deep learning techniques, a citizen-derived perceived livability index was created which was then combined with rapid flood exposure modelling through FastFlood. The results reveal that areas with the lowest livability scores -characterized by poor housing conditions, limited service access, and minimal green spaces- are also consistently the most exposed to frequent and severe flooding. In Nairobi, for instance, approximately 35% of built-up areas are flood-prone, with informal settlements like Kibera and Mathare facing disproportionate risks. Citizen science efforts validated the flood models, underscoring the critical role of local knowledge in capturing fine-scale flood dynamics invisible to remote sensing alone. The project demonstrates that liveability and environmental risk are deeply interrelated, and contribute to worsening urban vulnerability. By combining community mapping with scalable Earth Observation methods, this work delivers actionable methods for urban planners, humanitarian organizations, and local policymakers. Our results stress the importance of planning strategies that prioritize investments in flood mitigation, nature-based solutions, and resilient infrastructure for the most at-risk communities. Such communities are often omitted in official data. The needs and views of such vulnerable communities need to be included in supporting sustainable and inclusive urban development under increasing climate pressures.