March 9, 2020 Conference paper Open Access

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

DCAT Export

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    <dct:title>A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued>
    <dcat:keyword>Crisis Management</dcat:keyword>
    <dcat:keyword>Real-Time Fire Severity Assessment</dcat:keyword>
    <dcat:keyword>Image Recognition</dcat:keyword>
    <dcat:keyword>Object Detection</dcat:keyword>
    <dcat:keyword>Semantic Segmentation</dcat:keyword>
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        <foaf:name>European Commission</foaf:name>
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    <dct:description>&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;</dct:description>
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