Conference paper Open Access
Gerasimos Antzoulatos; Panagiotis Giannakeris; Ilias Koulalis; Anastasios Karakostas; Stefanos Vrochidis; Ioannis Kompatsiaris
{ "inLanguage": { "alternateName": "eng", "@type": "Language", "name": "English" }, "description": "<p>Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of<br>\nnatural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of<br>\nrisk-based decision support systems which encapsulate the technological achievements in Geographical Information<br>\nSystems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand,<br>\nthe rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of<br>\nlarge amounts of multimedia in social media or other online repositories has increased the interest in the image<br>\nunderstanding domain. Recent computer vision techniques endeavour to solve several societal problems with<br>\nsecurity and safety domains to be one of the most serious amongst others. Out of the millions of images that exist<br>\nonline in social media or news articles a great deal of them might include the existence of a crisis or emergency<br>\nevent. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based<br>\non knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a)<br>\nan Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover<br>\nfire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident<br>\nreports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a<br>\ncollection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion<br>\nlevel so as to showcase our method’s applicability.</p>", "license": "https://creativecommons.org/licenses/by/4.0/legalcode", "creator": [ { "affiliation": "Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)", "@type": "Person", "name": "Gerasimos Antzoulatos" }, { "affiliation": "Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)", "@type": "Person", "name": "Panagiotis Giannakeris" }, { "affiliation": "Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)", "@type": "Person", "name": "Ilias Koulalis" }, { "affiliation": "Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)", "@type": "Person", "name": "Anastasios Karakostas" }, { "affiliation": "Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)", "@type": "Person", "name": "Stefanos Vrochidis" }, { "affiliation": "Information Technologies Institute (ITI) - Centre for Research and Technology Hellas (CERTH)", "@type": "Person", "name": "Ioannis Kompatsiaris" } ], "headline": "A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents", "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", "datePublished": "2020-03-09", "url": "https://zenodo.org/record/3701479", "@type": "ScholarlyArticle", "keywords": [ "Crisis Management", "Real-Time Fire Severity Assessment", "Image Recognition", "Object Detection", "Semantic Segmentation" ], "@context": "https://schema.org/", "identifier": "https://doi.org/10.5281/zenodo.3701479", "@id": "https://doi.org/10.5281/zenodo.3701479", "workFeatured": { "url": "https://www.drrm.fralinlifesci.vt.edu/iscram2020/index.php", "alternateName": "ISCRAM 2020", "location": "Blacksburg, Virginia, USA", "@type": "Event", "name": "17th International Conference on Information Systems for Crisis Response and Management" }, "name": "A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents" }
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