ACTIVE DEEP LEARNING FOR EFFICIENT AERIAL IMAGE LABELLING IN DISASTER

Efficient analysis of aerial imagery has great potential to assist in disaster response efforts. In this paper we develop methods based on active learning to detect objects of interest. We formulate an approach based on fine-tuning deep networks for object recognition based on labels obtained from a human user. We demonstrate that this approach can effectively adapt features to recognize objects in aerial images taken after a disaster strikes.