Скачать или смотреть Data fusion with emergency management - Interview with Sonya McMullen

Conference Website: https://saiconference.com/IntelliSys

Data fusion methods have been traditionally
inclusive of Department of Defense and intelligence applications.
The extension of data fusion (DF) with emergency management
(EM), also referred to as crisis management, processes for
enhanced proactive decision-making has not been fully explored.
Hard sensor use in emergency management (e.g. satellites) has
evolved to include governmental Unmanned Aerial Systems
(UAS) but may have potential to include non-governmental
systems. Soft sensors in emergency management processes
(people) include the flow of information from governmental and
non-governmental sources. Data fusion emergency management
extension is a method of integrating governmental and nongovernmental
hard and soft information sources at varying levels
of information automation for enhanced managerial decision
making and utilization of limited resources. This paper
establishes a framework for the extension of data fusion to
emergency management in consideration of identified EM
themes, UAS integration, and behavioral limitations within an
autonomous yet collaborative network.

Abstract
Data fusion methods have been traditionally inclusive of Department of Defense and intelligence applications. The extension of data fusion (DF) with emergency management (EM), also referred to as crisis management, processes for enhanced proactive decision-making has not been fully explored. Hard sensor use in emergency management (e.g. satellites) has evolved to include governmental Unmanned Aerial Systems (UAS) but may have potential to include non-governmental systems. Soft sensors in emergency management processes (people) include the flow of information from governmental and non-governmental sources. Data fusion emergency management extension is a method of integrating governmental and non-governmental hard and soft information sources at varying levels of information automation for enhanced managerial decision making and utilization of limited resources. This paper establishes a framework for the extension of data fusion to emergency management in consideration of identified EM themes, UAS integration, and behavioral limitations within an autonomous yet collaborative network.