Localizing and quantifying infrastructure damage using class activation mapping approaches

Traditional post-disaster assessment of damage heavily relies on expensive geographic information system (GIS) data, especially remote sensing image data. In recent years, social media have become a rich source of disaster information that may be useful in assessing damage at a lower cost. Such information includes text (e.g., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams. The use of social media images to assess disaster damage is limited. We have recently proposed a novel approach, based on convolutional neural networks and class activation mapping, to locate building damage in a disaster image and to quantify the degree of the damage. In this paper, we study the usefulness of the proposed approach for other categories of infrastructure damage, specifically bridge and road damage, and compare two-class activation mapping approaches in this context. Experimental results show that our proposed approach enables the use of social network images for post-disaster infrastructure damage assessment and provides an inexpensive and feasible alternative to the more expensive GIS approach.