Machine learning algorithms are excellent at answering “yes” or “no” questions. For example, they can scan huge datasets and correctly tell us: Does this credit card transaction look fraudulent? Is there a cat in this photo?
But it’s not only the simple questions – they can also tackle nuanced and complex questions.
Today, machine learning algorithms can detect over 100 types of cancerous tumors more reliably than a trained human eye. Given this impressive accuracy, we started to wonder: what could machine learning tell us about where people live? In cities that are expanding at breathtaking rates and are at risk from natural disasters, could it warn us that a family’s wall might collapse during an earthquake or rooftop blow away during a hurricane?
Global Program for Resilient Housing
For a family, having a place to call home is everything. Housing tends to be a family’s most important asset – often, in fact, their only asset, especially for the poor. But more than a home, housing is also the workplace, collateral for loans and an important vehicle for job creation. In the U.S., housing contributes more than 15% of the GDP.
The dream of housing, however, can quickly turn into a nightmare – for both families and for governments. Disasters can erase decades of progress in reform and poverty reduction in a matter of seconds, hurting the poor and vulnerable the most. A review of the World Bank’s Post-Disaster Needs Assessments (PDNAs) since 2000 shows that housing comprises 40%-90% of damages to private property.