Microsoft building footprint application To detect human exposure due to tsunami

Abstract

Tsunami events at night are more prone to causing fatalities because humans are resting in residential buildings (houses). In this study, residential buildings were extracted using the Microsoft Building Footprint (MBF), which resulted from applying artificial intelligence technology. This study aims to analyze the number of people exposed to tsunamis at night using MBF. The tsunami modeling was carried out using the Berryman method. Sentinel 2-A Image extracted from Google Earth Engine. The results of the inundation modeling analysis show that the total inundated area is 717 Ha or 17.34% of the total area. The results of the MBF accuracy analysis on the entire data are a Precision of 99.02%, Recall of 98.40%, and F1 score of 98.71%. The results of the MBF error analysis are False Positive 0.97%, False Negative 1.60%, and Intersection of Union 0.12%. The number of people exposed is 2,749, or 6.32% of the total population.