If you’ve ever crashed a car before then you know how it awkward and frustrating it can be. If you’re lucky and everything is straight forward, then it’s alright but if not, you can be in for a world of pain. If you’ve ever been through a natural disaster though, it’s just a pain from start to end.

First comes the blame game, then comes the proof

From the perspective of the Government and the Insurance industry, the use case is quite obvious as the occurrence of disasters such as hurricanes, earthquakes, floods need to be identified quickly. These kind of events don’t just decimate the buildings when they occur, but they decimate the entire environment around it too.

Obtaining accurate data to help plan for an effective response has been a challenge because collecting and extracting data has been a slow and labour intensive operation. Given that, any response is currently quite slow. Drones and Satellite Imagery have improved the problem somewhat, but a lot of data still has to be manually collated.

As a result of this problem, machine learning fits naturally here to automate the role of detecting damages caused by disasters. Machine Learning can also offer a solution for future disasters because a well built model can generalise, but also improve it’s recognition capability as the amount of data increases.

Existing work in the field focuses largely on single-event disasters but recently, Google produced a custom dataset spanning 3 disasters (Haiti, Mexico and Indonesian earthquakes), from which they applied a CNN and measured how well it could generalise.

#data-science #machine-learning #python #artificial-intelligence #technology