Disaster Management

Dear Reader,  

We are living in the midst of a revolution. Supervised learning, a branch of Machine learning allows engineers to develop models that can train themselves. In turn, these models are helping solve crisis management problems before disaster strikes. 

Technologists have long modeled data to harness machine learning for disaster relief. After the Chernobyl crisis, scientists analyzed satellite imagery and weather data to track the flow of radiation from the reactor. Today’s algorithms far outpace their predecessors in analytic and predictive powers. Machine learning models are able to deliver more granular predictions. NASA has developed the Landslide Hazard Assessment for Situational Awareness (LHASA) Model. Data from the Global Precipitation Measurement (GPM) is fed into LHASA in three-hour intervals. If a landslide-prone area is experiencing heavy rain, LHASA then issues a warning. Analysts then channel that information to the appropriate agencies, providing near-real-time risk assessments. 

Roofing material is a major risk factor in resilience to natural disasters. So, a model that can predict it is also one that can predict which buildings are most at risk during an emergency. In Guatemala, models are identifying “soft-story” buildings–those most likely to collapse during an earthquake. “Forecast funding” can mitigate damage by providing the most vulnerable with cash assistance to prepare for disaster. Bangladesh and Nepal are nations that are already implementing this strategy. 

Natural disasters, such as earthquakes, hurricanes and floods affect large areas and millions of people, but responding to such disasters is a massive logistical challenge. Crisis responders, including governments, NGOs, and UN organizations, need fast access to comprehensive and accurate assessments in the aftermath of disasters to plan how best to allocate limited resources. To help mitigate the impact of such disasters, Google in partnership with the United Nations World Food Program (WFP) Innovation Accelerator has created "Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks", which details a machine learning (ML) approach to automatically process satellite data to generate building damage assessments. As per Google this work has the potential to drastically reduce the time and effort required for crisis workers to produce damage assessment reports. In turn, this would reduce the turnaround times needed to deliver timely disaster aid to the most severely affected areas, while increasing the overall coverage of such critical services. The World Food Programme was awarded the 2020 Nobel Peace Prize and they thanked Google and its team of engineers in pioneering the development of artificial intelligence to revolutionise humanitarian operations.        

The application of machine learning techniques to satellite imagery is revolutionizing disaster relief. Crisis maps and image comparisons are helping relief organizations to deliver aid with precision. 

Credits : Akhil Handa Prithwijit Ghosh