Gerasimos Antzoulatos, Panagiotis Giannakeris, Ilias Koulalis, Anastasios Karakostas, Stefanos Vrochidis, & Ioannis Kompatsiaris. (2020). A Multi-Layer Fusion Approach For Real-Time Fire Severity Assessment Based on Multimedia Incidents. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 75–89). Blacksburg, VA (USA): Virginia Tech.
Abstract: Shock forest fires have short and long-terms devastating impact on the sustainable management and viability of natural, cultural and residential environments, the local and regional economies and societies. Thus, the utilisation of risk-based decision support systems which encapsulate the technological achievements in Geographical Information Systems (GIS) and fire growth simulation models have rapidly increased in the last decades. On the other hand, the rise of image and video capturing technology, the usage mobile and wearable devices, and the availability of large amounts of multimedia in social media or other online repositories has increased the interest in the image understanding domain. Recent computer vision techniques endeavour to solve several societal problems with security and safety domains to be one of the most serious amongst others. Out of the millions of images that exist online in social media or news articles a great deal of them might include the existence of a crisis or emergency event. In this work, we propose a Multi-Layer Fusion framework, for Real-Time Fire Severity Assessment, based on knowledge extracted from the analysis of Fire Multimedia Incidents. Our approach consists of two levels: (a) an Early Fusion level, in which state-of-the-art image understanding techniques are deployed so as to discover fire incidents and objects from images, and (b) the Decision Fusion level which combines multiple fire incident reports aiming to assess the severity of the ongoing fire event. We evaluate our image understanding techniques in a collection of public fire image databases, and generate simulated incidents and feed them to our Decision Fusion level so as to showcase our method's applicability.
|
Nguyen Bang Tran, Mihai Tanase, Lauren Bennett, & Cristina Aponte. (2018). Evaluation of spectral indices for assessing fire severity in Victorian temperate forests, Australia. In Kristin Stock, & Deborah Bunker (Eds.), Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. (pp. 213–222). Albany, Auckland, New Zealand: Massey Univeristy.
Abstract: ABSTRACT Victorian temperate forests, characterized by variable wildfire response traits, such an analysis is missing hindering fire impact estimation over large areas on operational basis. To close this gap, we have evaluated 10 remotely sensed indices across eight areas affected by wildfires in 1998, 2006, 2007, and 2009 which comprise 13 forest types. The analysis was carried out at forest type level and as a function of the regeneration strategies (seeders, basal and epicormic reporters) and structure (tree height and canopy cover) with the six forest groups. Index performance was evaluated by (i) examining index response across four fire severity levels, (ii) the separability index, and (iii) the optimality values analysis. A ranking scoring system was used to compare the index performance to distinguish among severity classes. Initial results demonstrated that there hasn't been a consistency of the best indices capacity but there a consistently worse index among forest groups.
|
