A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents

Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of
natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of
risk-based decision support systems which encapsulate the technological achievements in Geographical Information
Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand,
the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of
large amounts of multimedia in social media or other online repositories has increased the interest in the image
understanding domain. Recent computer vision techniques endeavour to solve several societal problems with
security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist
online in social media or news articles a great deal of them might include the existence of a crisis or emergency
event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based
on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a)
an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover
fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident
reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a
collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion
level so as to showcase our method’s applicability.