遥感
鉴定(生物学)
计算机科学
背景(考古学)
数据科学
城市规划
社会化媒体
地理
工程类
万维网
土木工程
植物
考古
生物
作者
Xiaoyue Xing,Bailang Yu,Chaogui Kang,Bo Huang,Jianya Gong,Yu Liu
出处
期刊:IEEE Geoscience and Remote Sensing Magazine
[Institute of Electrical and Electronics Engineers]
日期:2024-03-01
卷期号:12 (1): 108-137
被引量:2
标识
DOI:10.1109/mgrs.2023.3343968
摘要
Urban studies require a rich set of information sources and techniques that enable a comprehensive depiction of urban environments. Remote sensing captures physical characteristics of urban landscapes, while social sensing collects data from social media and digital devices to reflect human activities. The combination of remote sensing and social sensing has been employed to investigate urban environments, urban dynamics, and the well-being of city residents. This review explores leading ideas and methodologies of the synergy between remote sensing and social sensing in a broad context of urban studies. The synergy involves leveraging the benefits of remote sensing and social sensing to gain a deeper understanding of urban characteristics than can be acquired through any single sensing approach. Two types of synergy are identified, namely, "transfer-based synergy" and "integration-based synergy." The former transfers ideas and techniques between remote sensing and social sensing based on their similarity. The latter integrates these sensing ways based on their complementary advantages. The motivations and methods are summarized to show how such synergy is suited for investigating the complex nature of urban systems. Typical applications of the synergy include land use and functional zone mapping, special land use identification, estimation of natural and socioeconomic elements, and emergency response. We also identify data quality issues, refinement of methodologies, and expansion of applications that still pose challenges and are worth future research. This review lays a foundation for synergizing remote sensing and social sensing, offering researchers guidance to reexamine and reconceptualize the city from multiple sensing perspectives.
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