Recursive temporal-spatial information fusion

2005 ◽  
Author(s):  
L. Hong
Keyword(s):  
Information Fusion ◽  
Spatial Information

Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

2012 ◽  
Vol 13 (7) ◽  
pp. 520-533 ◽  
Author(s):  
Jamal Ghasemi ◽  
Mohammad Reza Karami Mollaei ◽  
Reza Ghaderi ◽  
Ali Hojjatoleslami
Keyword(s):  
Brain Tissue ◽  
Information Fusion ◽  
Spatial Information ◽  
Tissue Segmentation ◽  
Dempster Shafer Theory ◽  
Brain Tissue Segmentation ◽  
Shafer Theory

scholarly journals Designing information fusion for the encoding of visual–spatial information

Ergonomics ◽  
2008 ◽  
Vol 51 (6) ◽  
pp. 775-797 ◽  
Author(s):  
Samuel M. Waldron ◽  
John Patrick ◽  
Geoffrey B. Duggan ◽  
Simon Banbury ◽  
Andrew Howes
Keyword(s):  
Information Fusion ◽  
Spatial Information ◽  
Visual Spatial

scholarly journals Fire Risk Assessment in Dense Urban Areas Using Information Fusion Techniques

2019 ◽  
Vol 8 (12) ◽  
pp. 579 ◽  
Author(s):  
Zohreh Masoumi ◽  
John van L.Genderen ◽  
Jamshid Maleki
Keyword(s):  
Risk Assessment ◽  
Information Fusion ◽  
Urban Areas ◽  
Spatial Information ◽  
Fire Risk ◽  
Mitigation Strategies ◽  
Risk Level ◽  
High Rise ◽  
High Rise Building ◽  
Fire Risk Assessment

A comprehensive fire risk assessment is very important in dense urban areas as it provides an estimation of people at risk and property. Fire policy and mitigation strategies in developing countries are constrained by inadequate information, which is mainly due to a lack of capacity and resources for data collection, analysis, and modeling. In this research, we calculated the fire risk considering two aspects, urban infrastructure and the characteristics of a high-rise building for a dense urban area in Zanjan city. Since the resources for this purpose were rather limited, a variety of information was gathered and information fusion techniques were conducted by employing spatial analyses to produce fire risk maps. For this purpose, the spatial information produced using unmanned aerial vehicles (UAVs) and then attribute data (about 150 characteristics of each high-rise building) were gathered for each building. Finally, considering high-risk urban infrastructures, like the position of oil and gas pipes and electricity lines and the fire safety analysis of high-rise buildings, the vulnerability map for the area was prepared. The fire risk of each building was assessed and its risk level was identified. Results can help decision-makers, urban planners, emergency managers, and community organizations to plan for providing facilities and minimizing fire hazards and solve some related problems to reduce the fire risk. Moreover, the results of sensitivity analysis (SA) indicate that the social training factor is the most effective causative factor in the fire risk.

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