March 9, 2020 Conference paper Open Access

Gerasimos Antzoulatos; Panagiotis Giannakeris; Ilias Koulalis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris

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