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.

传统的数据收集方法（如遥感和现场调查）通常在灾难事件发生期间或之后无法及时提供信息。社会感知使所有公民都能成为大型传感器网络的一部分，该网络成本低廉，功能更全面，并且始终在广播态势感知信息。但是，通过社交感知收集的数据通常庞大，异构，嘈杂，从某些方面来看不可靠，源源不断，并且常常缺乏地理空间参考信息。这些问题共同构成了在极端胁迫下充分利用社会意识进行应急管理决策的巨大挑战。同时，大数据计算方法和技术，例如高性能计算，深度学习，以及多源数据融合已成为使用社交感知来及时了解灾难事件的影响和响应的关键组成部分。本期特刊记录了利用社交感知和大数据计算来支持灾难管理的最新进展。这些论文中专门分析了社会感知数据在与灾害相关的信息提取，影响区域评估，人口分布，发生模式，社交媒体使用中的地理差异以及纳入更大的决策支持系统中的一些希望和陷阱。