3701479
doi
10.5281/zenodo.3701479
oai:zenodo.org:3701479
user-eu
Panagiotis Giannakeris
Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)
Ilias Koulalis
Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)
Anastasios Karakostas
Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)
Stefanos Vrochidis
Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)
Ioannis Kompatsiaris
Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)
A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents
Gerasimos Antzoulatos
Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Crisis Management
Real-Time Fire Severity Assessment
Image Recognition
Object Detection
Semantic Segmentation
<p>Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of<br>
natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of<br>
risk-based decision support systems which encapsulate the technological achievements in Geographical Information<br>
Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand,<br>
the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of<br>
large amounts of multimedia in social media or other online repositories has increased the interest in the image<br>
understanding domain. Recent computer vision techniques endeavour to solve several societal problems with<br>
security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist<br>
online in social media or news articles a great deal of them might include the existence of a crisis or emergency<br>
event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based<br>
on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a)<br>
an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover<br>
fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident<br>
reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a<br>
collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion<br>
level so as to showcase our method’s applicability.</p>
Zenodo
2020-03-09
info:eu-repo/semantics/conferencePaper
3701478
user-eu
award_title=Enhancing decision support and management services in extreme weather climate events; award_number=700475; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/700475; funder_id=00k4n6c32; funder_name=European Commission;
award_title=Enhancing Standardisation strategies to integrate innovative technologies for Safety and Security in existing water networks; award_number=832876; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/832876; funder_id=00k4n6c32; funder_name=European Commission;
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https://zenodo.org/records/3701479/files/A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents.pdf
public
10.5281/zenodo.3701478
isVersionOf
doi