Enhancing Community Resilience and Disaster Preparedness Through AI-Assisted Open Mapping

Africa confronts a dual crisis: escalating natural hazards like floods and droughts, compounded by a severe lack of foundational geospatial data needed for disaster response. This “data gap” traps communities in reactive cycles instead of proactive resilience. This chapter proposes an AI-Assisted Open Mapping Ecosystem (AIA-OME) to address this. The framework integrates artificial intelligence with community platforms like OpenStreetMap in a four-stage cycle: remote data acquisition, AI-powered feature extraction, community validation using local knowledge, and data integration for disaster risk reduction. This human-centered approach balances AI scalability with contextual accuracy. It incorporates ethical oversight, data sovereignty, and community feedback for continuous model improvement. Conceptual findings suggest gains in efficiency, data quality, and community empowerment. The chapter calls for empirical validation through pilot projects, emphasizing interdisciplinary collaboration and sustainable governance to ensure equitable outcomes in vulnerable African regions.