Conventional wisdom would have it that a dollar spent in reducing the risk of damage from a natural calamity would take greater precedence over the amounts spent in repairing damage. European Commission research, presented at the Global Disaster Relief and Development Summit of 2017, shows every $1 spent on mitigation saves $4 in recovery and reconstruction. From 1991 to 2010, however, only a small fraction (12.7%) of global disaster-related expenditures ($3.3 trillion) has been used for risk mitigation—the remainder, 87.3%, was spent on emergency response, reconstruction, and rehabilitation. Preparing better against known and new disasters that will not only reduce the numbers of lives lost, but also lead to reduced expenditures for rebuilding lost infrastructure in the long-run. This is where artificial intelligence, big data and digital equipment such as drones come in. The collection and analysis of the necessary visual and building measurement data can greatly bring down the devastation potential of natural disasters.
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In Guatemala City, images and algorithms were used to locate “soft-storey” buildings—buildings at least two storeys high that have a structurally weak first floors. After scanning 4,967 homes, AI detected 503 possible soft-storey houses with 85% accuracy. Such household-level survey and data can then be used by governments, home-owners, and the private sector to make important policy decisions; this can even extend beyond disaster risk mitigation. It can ease and streamline rehabilitation and redevelopment programmes, aid in delivery of post-disaster needs and lessen the economic toll on homeowners, real estate developers and governments. With urbanisation set to become much faster-paced, climate change impact becoming more forceful, and with land and housing coming under increased pressure from expanding populations—more so in the developing world—harnessing machine learning could pay off big for sustainable housing and lessening the costs of natural disasters.