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Social Sensing and Big Data Computing for Disaster Management
Loot Price: R1,372
Discovery Miles 13 720
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Social Sensing and Big Data Computing for Disaster Management
Expected to ship within 12 - 19 working days
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Social Sensing and Big Data Computing for Disaster Management
captures recent advancements in leveraging social sensing and big
data computing for supporting disaster management. Specifically,
analysed within this book are some of the promises and pitfalls of
social sensing data for disaster relevant information extraction,
impact area assessment, population mapping, occurrence patterns,
geographical disparities in social media use, and inclusion in
larger decision support systems. Traditional data collection
methods such as remote sensing and field surveying often fail to
offer timely information during or immediately following disaster
events. Social sensing enables all citizens to become part of a
large sensor network which is low cost, more comprehensive, and
always broadcasting situational awareness information. However,
data collected with social sensing is often massive, heterogeneous,
noisy, and unreliable in some aspects. It comes in continuous
streams, and often lacks geospatial reference information.
Together, these issues represent a grand challenge toward fully
leveraging social sensing for emergency management decision making
under extreme duress. Meanwhile, big data computing methods and
technologies such as high-performance computing, deep learning, and
multi-source data fusion become critical components of using social
sensing to understand the impact of and response to the disaster
events in a timely fashion. This book was originally published as a
special issue of the International Journal of Digital Earth.
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