A Systematic Review of Government‐Funded AI Projects for Disaster Management in Taiwan: Trends and Priorities

ABSTRACT

While AI in disaster management has been widely reviewed, current research often overlooks how domestic AI disaster research portfolios can be systematically mapped and compared with international patterns. To address this gap, we develop a systematic mapping framework encompassing implementation years, funding sources, disciplinary fields, disaster types, operational stages, and thematic foci. Using the 309 government‐funded AI projects for disaster management in Taiwan as a case study, we demonstrate how this approach not only identifies key convergences and divergences with international trends but also highlights the configuration of national research priorities. Our findings indicate that the project timeline of Taiwan broadly aligns with global trends, with a notable surge in activity around 2018. However, limited attention has been given to the humanities and social sciences, emerging hazards, and post‐disaster recovery. We further synthesize key priorities and underexplored areas. Moreover, academically driven projects dominate the portfolio and demonstrate innovation, but they may face challenges in practical application due to insufficient coordination with government agencies. By providing a replicable framework for cross‐national comparison, this study offers a transferable approach for other nations to evaluate their national AI disaster research agenda.