WHAT THE RESEARCH IS:
A framework for using convolutional neural networks (CNNs) on satellite imagery to identify the areas most severely affected by a disaster. This new method has the potential to produce more accurate information in far less time than current manual methods. Ultimately, the goal of this research is to allow rescue workers to quickly identify where aid is needed most, without relying on manually annotated, disaster-specific data sets.
HOW IT WORKS:
Researchers from Facebook and CrowdAI train models on CNNs to detect human-made features, such as roads. Existing approaches for disaster impact analysis require data sets that can be expensive to produce because they require time-consuming manual annotation (for instance, annotating buildings damaged by fire as a new class) to train on. This new method only uses general road and building data sets. These are readily available and can be scalable to other, …[Read more]