UPDATE: Zenodo migration postponed to Oct 13 from 06:00-08:00 UTC. Read the announcement.

Conference paper Open Access

Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams

Thomas Papadimos; Nick Pantelidis; Stelios Andreadis; Aristeidis Bozas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris

The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019.

Files (699.5 kB)
Name Size
Papadimos_et_al_Real-time_Alert_Framework_for_Fire_Incidents.pdf
md5:4d46d07055edb2aeb67505f2ffbf8c4b
699.5 kB Download
136
125
views
downloads
All versions This version
Views 136136
Downloads 125125
Data volume 87.4 MB87.4 MB
Unique views 121121
Unique downloads 114114

Share

Cite as