In regions like Caribbean, which faces a significant risk from natural calamities such as hurricanes, floods, and earthquake, such forces can have devastating results. This is particularly true where the buildings and houses do not follow the modern standards of construction. Such constructions can be usually seen in informal settlements or slum areas. There is a provision of retrofitting the buildings to equip them for potential disasters. However, the conventional method of identifying high-risk construction site involves on foot travel. It may take weeks and months and costs millions of dollars.
With the advancements in technology, this is also an area in which the artificial intelligence can help. One specifically relevant characteristic is of the roof construction material. The roof construction material is among the main risk factors during the time of an earthquake or a hurricane. It is also a predictor of some other risk factors such as building material, which cannot be easily seen from the air.
An Innovative Challenge
Keeping this in mind, the Driven Data has come up with an innovative challenge. The objective of the challenge is to provide the aerial images of the identified buildings in St. Lucia, Colombia, and Guatemala. The images of the building are use to classify the roof materials. This will help in determining the gravity of the risk factor of these buildings.
The idea is to develop machine learning or the artificial intelligence driven models that can accurately map the disaster risk. The images captured through drones will help in quicker as well as cheaper prioritization of the building inspection. This will help in nullifying the risk factor and also help in saving valuable resources in terms of money and human hours. Moreover, such machine learning models will have a quicker impact on getting valuable insights on the inspection of such disaster-prone buildings
