Technical and methodological enhancement of hazards and disaster research is identified
as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking
and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine
learning, telecom and network services, accident and hot spot analysis, smart city urban planning,
transportation planning, and environmental impact analysis, are the technological components of
societal change, having significant implications for research on the societal response to hazards
and disasters. Social science researchers have used various technologies and methods to examine
hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They
have employed both quantitative and qualitative data collection and data analysis strategies. This
study provides an overview of the current applications of AI in disaster management during its
four phases and how AI is vital to all disaster management phases, leading to a faster, more concise,
equipped response. Integrating a geographic information system (GIS) and remote sensing (RS)
into disaster management enables higher planning, analysis, situational awareness, and recovery
operations. GIS and RS are commonly recognized as key support tools for disaster management.
Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments
in making quick decisions after natural disasters
