Providing a greater precision of Situational Awareness of urban floods through Multimodal Fusion

2021 ◽  
pp. 115923
Author(s):  
Thiago Aparecido Gonçalves da Costa ◽  
Rodolfo Ipolito Meneguette ◽  
Jó Ueyama
Keyword(s):  
Situational Awareness ◽  
Multimodal Fusion ◽  
Urban Floods

scholarly journals Disaster Image Classification by Fusing Multimodal Social Media Data

2021 ◽  
Vol 10 (10) ◽  
pp. 636
Author(s):  
Zhiqiang Zou ◽  
Hongyu Gan ◽  
Qunying Huang ◽  
Tianhui Cai ◽  
Kai Cao
Keyword(s):  
Social Media ◽  
Situational Awareness ◽  
Multimodal Fusion ◽  
Fusion Model ◽  
Multimodal Data ◽  
Disaster Assessment ◽  
Social Media Platforms ◽  
Text Information ◽  
Textual Features ◽  
Media Data

Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading content that needs to be filtered out. Existing work has mostly used unimodal methods to classify disaster messages. In other words, these methods treated the image and textual features separately. While a few methods adopted multimodality to deal with the data, their accuracy cannot be guaranteed. This research seamlessly integrates image and text information by developing a multimodal fusion approach to identify useful disaster images collected from social media platforms. In particular, a deep learning method is used to extract the visual features from social media, and a FastText framework is then used to extract the textual features. Next, a novel data fusion model is developed to combine both visual and textual features to classify relevant disaster images. Experiments on a real-world disaster dataset, CrisisMMD, are performed, and the validation results demonstrate that the method consistently and significantly outperforms the previously published state-of-the-art work by over 3%, with a performance improvement from 84.4% to 87.6%.

Maintaining Situational Awareness: The Role of Visual Attention

1999 ◽  
Author(s):  
Alex Chaparro ◽  
Loren Groff ◽  
Kamala Tabor ◽  
Kathy Sifrit ◽  
Leo J. Gugerty
Keyword(s):  
Visual Attention ◽  
Situational Awareness

Review on Fog Based Spectrum Sensing for Artificial Intelligence

2018 ◽  
pp. 66-70
Author(s):  
A. Rethina Palin ◽  
I. Jeena Jacob
Keyword(s):  
Bandwidth Allocation ◽  
Situational Awareness ◽  
Cluster Head ◽  
Mesh Network ◽  
Network Systems ◽  
Markov Logic ◽  
Wireless Mesh ◽  
Local Environments ◽  
Reactive Routing ◽  
The Hierarchical Structure

Wireless Mesh Network (MWN) could be divided into proactive routing, reactive routing and hybrid routing, which must satisfy the requirements related to scalability, reliability, flexibility, throughput, load balancing, congestion control and efficiency. DMN (Directional Mesh Network) become more adaptive to the local environments and robust to spectrum changes. The existing computing units in the mesh network systems are Fog nodes, the DMN architecture is more economic and efficient since it doesn’t require architecture- level changes from existing systems. The cluster head (CH) manages a group of nodes such that the network has the hierarchical structure for the channel access, routing and bandwidth allocation. The feature extraction and situational awareness is conducted, each Fog node sends the information regarding the current situation to the cluster head in the contextual format. A Markov logic network (MLN) based reasoning engine is utilized for the final routing table updating regarding the system uncertainty and complexity.

Situational Awareness Applied to Dynamic Security in Power Systems

2017 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Sandra Camila Garzon ◽  
Mario Alberto Rios ◽  
Oscar Gomez
Keyword(s):  
Power Systems ◽  
Situational Awareness ◽  
Dynamic Security

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

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