Multi-source multimodal deep learning to improve situation awareness : an application of emergency traffic management : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

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Title

Multi-source multimodal deep learning to improve situation awareness : an application of emergency traffic management : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Content partner

Massey University

Collection

Massey Research Online

Description

Traditionally, disaster management has placed a great emphasis on institutional warning systems, and people have been treated as victims rather than active participants. However, with the evolution of communication technology, today, the general public significantly contributes towards performing disaster management tasks challenging traditional hierarchies in information distribution and acquisition. With mobile phones and Social Media (SM) platforms widely being used, people in disaster sce...

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Format

Research paper

Research format

Thesis

Thesis level

Doctoral

Date created

2023

Creator

Hewa Algiriyage, Rangika Nilani

URL

https://hdl.handle.net/10179/18268

Related subjects

Traffic congestion / Management / Situational awareness / Social media / Data Processing / Emergency management / Information services / Deep learning (Machine learning) / 350703 Disaster and emergency management / 461103 Deep learning

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