Conference paper Open Access
Gerasimos Antzoulatos; Panagiotis Giannakeris; Ilias Koulalis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="DOI">10.5281/zenodo.3701479</identifier> <creators> <creator> <creatorName>Gerasimos Antzoulatos</creatorName> <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation> </creator> <creator> <creatorName>Panagiotis Giannakeris</creatorName> <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation> </creator> <creator> <creatorName>Ilias Koulalis</creatorName> <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation> </creator> <creator> <creatorName>Anastasios Karakostas</creatorName> <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation> </creator> <creator> <creatorName>Stefanos Vrochidis</creatorName> <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation> </creator> <creator> <creatorName>Ioannis Kompatsiaris</creatorName> <affiliation>Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</affiliation> </creator> </creators> <titles> <title>A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents</title> </titles> <publisher>Zenodo</publisher> <publicationYear>2020</publicationYear> <subjects> <subject>Crisis Management</subject> <subject>Real-Time Fire Severity Assessment</subject> <subject>Image Recognition</subject> <subject>Object Detection</subject> <subject>Semantic Segmentation</subject> </subjects> <dates> <date dateType="Issued">2020-03-09</date> </dates> <language>en</language> <resourceType resourceTypeGeneral="Text">Conference paper</resourceType> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3701479</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3701478</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><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&rsquo;s applicability.</p></description> </descriptions> <fundingReferences> <fundingReference> <funderName>European Commission</funderName> <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier> <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/700475/">700475</awardNumber> <awardTitle>Enhancing decision support and management services in extreme weather climate events</awardTitle> </fundingReference> <fundingReference> <funderName>European Commission</funderName> <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier> <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/832876/">832876</awardNumber> <awardTitle>Enhancing Standardisation strategies to integrate innovative technologies for Safety and Security in existing water networks</awardTitle> </fundingReference> </fundingReferences> </resource>
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