Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data

TL;DR

This paper introduces an unsupervised deep learning approach using knowledge distillation to detect and localize damages in social media images during disasters, effectively handling novel disaster types without requiring damage annotations.

Contribution

The paper proposes a novel unsupervised KD-based neural network architecture for damage detection and localization in disaster images, overcoming dataset limitations and generalization issues of supervised methods.

Findings

Outperforms state-of-the-art methods in damage detection and localization

Effective in identifying damages across different disaster types

Demonstrates robustness on benchmark datasets

Abstract

Social media plays a significant role in sharing essential information, which helps humanitarian organizations in rescue operations during and after disaster incidents. However, developing an efficient method that can provide rapid analysis of social media images in the early hours of disasters is still largely an open problem, mainly due to the lack of suitable datasets and the sheer complexity of this task. In addition, supervised methods can not generalize well to novel disaster incidents. In this paper, inspired by the success of Knowledge Distillation (KD) methods, we propose an unsupervised deep neural network to detect and localize damages in social media images. Our proposed KD architecture is a feature-based distillation approach that comprises a pre-trained teacher and a smaller student network, with both networks having similar GAN architecture containing a generator and a discriminator. The student network is trained to emulate the behavior of the teacher on training input samples, which, in turn, contain images that do not include any damaged regions. Therefore, the student network only learns the distribution of no damage data and would have different behavior from the teacher network-facing damages. To detect damage, we utilize the difference between features generated by two networks using a defined score function that demonstrates the probability of damages occurring. Our experimental results on the benchmark dataset confirm that our approach outperforms state-of-the-art methods in detecting and localizing the damaged areas, especially for novel disaster types.