Introduction to social sensing and big data computing for disaster management. Issue 11 (2nd November 2019) - Li, Zhenlong; Huang, Qunying; Emrich, Christopher T.
ABSTRACT: 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, unreliable from some aspects, 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 special issue captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically analyzed within these papers 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.