Sensing Urban Dynamics by Fusing Multi-sourced Spatiotemporal Big Data

City is the place aggregated massive human activities. City is the exchange hub of population flow, goods flow, information flow and currency flow, which is highly dynamic and complex. Smart city provides various tools to acquire spatiotemporal big data, such as satellite and drone remote sensing, mobile sensing, social sensing, crowdsourcing sensing, etc., which enable us to sense urban dynamics. This paper introduces the framework of urban dynamic sensing, describes the typical applications of spatial dynamics, human behavior dynamics and space-behavior interaction dynamics, and discusses the problems, such as the uncertainty in spatiotemporal big data, the multi-view ensemble learning in urban sensing, the verification of the urban dynamic results and the cascading influence of multi-urban factors. Outlooking the future, the study of urban dynamics should combine with real-time Internet of things data to sense multi-dimensional, multi-spatiotemporal resolution urban dynamic to enable refined urban governance and to effectively solve urban problems.