March 9, 2020 Conference paper Open Access

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

MARC21 XML Export

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    <subfield code="a">Crisis Management</subfield>
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    <subfield code="a">Real-Time Fire Severity Assessment</subfield>
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    <subfield code="a">Image Recognition</subfield>
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    <subfield code="d">May 24, 2020 - May 27, 202</subfield>
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    <subfield code="a">17th International Conference on Information Systems for Crisis Response and Management</subfield>
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    <subfield code="n">Track 1 - AI Systems for Crisis and Risks</subfield>
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    <subfield code="u">Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</subfield>
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    <subfield code="a">Stefanos Vrochidis</subfield>
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    <subfield code="a">Gerasimos Antzoulatos</subfield>
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    <subfield code="a">A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents</subfield>
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    <subfield code="a">&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;</subfield>
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