Using AI to measure disaster damage costs: Methodology and the example of the 2018 Sulawesi earthquake

Abstract

As disasters grow in frequency and intensity, the opportunities to apply Artificial Intelligence (Al) to disaster risk reduction are becoming increasingly prominent. This paper discusses various Al-based approaches including crowdsourcing, Internet of Things (loT), aerial imagery analysis, videos from unmanned aerial vehicles (UAVs), as well as airborne and terrestrial Light Detection and Ranging (LIDAR). It also analyses the methodology of Al- and satellite imagery-based approaches to measuring the costs of disasters, using the case of the 2018 earthquake and tsunami in Sulawesi, Indonesia as an example.