Conference paper Open Access
Gerasimos Antzoulatos; Panagiotis Giannakeris; Ilias Koulalis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Crisis Management</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Real-Time Fire Severity Assessment</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Image Recognition</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Object Detection</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Semantic Segmentation</subfield> </datafield> <controlfield tag="005">20200617083915.0</controlfield> <controlfield tag="001">3701479</controlfield> <datafield tag="711" ind1=" " ind2=" "> <subfield code="d">May 24, 2020 - May 27, 202</subfield> <subfield code="g">ISCRAM 2020</subfield> <subfield code="a">17th International Conference on Information Systems for Crisis Response and Management</subfield> <subfield code="c">Blacksburg, Virginia, USA</subfield> <subfield code="n">Track 1 - AI Systems for Crisis and Risks</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</subfield> <subfield code="a">Panagiotis Giannakeris</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</subfield> <subfield code="a">Ilias Koulalis</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</subfield> <subfield code="a">Anastasios Karakostas</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</subfield> <subfield code="a">Stefanos Vrochidis</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</subfield> <subfield code="a">Ioannis Kompatsiaris</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">3653279</subfield> <subfield code="z">md5:d2fa3c4a715f333c6940df8b1d340151</subfield> <subfield code="u">https://zenodo.org/record/3701479/files/A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="y">Conference website</subfield> <subfield code="u">https://www.drrm.fralinlifesci.vt.edu/iscram2020/index.php</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2020-03-09</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="o">oai:zenodo.org:3701479</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)</subfield> <subfield code="a">Gerasimos Antzoulatos</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents</subfield> </datafield> <datafield tag="536" ind1=" " ind2=" "> <subfield code="c">700475</subfield> <subfield code="a">Enhancing decision support and management services in extreme weather climate events</subfield> </datafield> <datafield tag="536" ind1=" " ind2=" "> <subfield code="c">832876</subfield> <subfield code="a">Enhancing Standardisation strategies to integrate innovative technologies for Safety and Security in existing water networks</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><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></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">doi</subfield> <subfield code="i">isVersionOf</subfield> <subfield code="a">10.5281/zenodo.3701478</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.5281/zenodo.3701479</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">conferencepaper</subfield> </datafield> </record>
All versions | This version | |
---|---|---|
Views | 47 | 47 |
Downloads | 34 | 34 |
Data volume | 124.2 MB | 124.2 MB |
Unique views | 38 | 38 |
Unique downloads | 31 | 31 |