信息技术与定位技术的快速发展不仅推动了海量地理空间大数据的持续增长，也引发了对利用此类数据补充遥感影像的关注。已有诸多研究致力于利用地理空间大数据识别人类活动模式，并结合遥感影像开展城市与环境研究。然而，地理空间大数据的代表性与局部性问题，以及融合方法等仍需进一步探索。本文首先回顾了由地理空间大数据驱动的数据挖掘与分析技术的创新进展及其在遥感应用中的发展。此外，简要介绍了“社会感知”与“城市计算”两个流行概念。随后，重点阐述了地理空间大数据在挖掘人类活动动态与社会经济特征方面的作用，以及其与遥感数据结合用于各类研究的可行性。最后，通过若干实证案例，展示了遥感与地理空间大数据在土地利用提取、环境与灾害监测、社会经济动态感知等方面的融合应用。所提供的案例与讨论表明，遥感与地理空间大数据的集成具有高效性与互补性，可从多维度和多尺度支持决策制定。

The rapid development of information technology and location techniques not only leads to an increasing growth of massive geospatial big data but also raises the attention of using these data to complement with remote sensing images. Many efforts have been made to utilize geospatial big data to identify human activity patterns and carry out urban and environmental researches, integrating with remote sensing images. Nonetheless, there are still many issues, including the representativeness and locality of geospatial big data, as well as the fusion methods, remain to be further explored. In this article, we first reviewed the innovation and proceedings of data mining and analyzing techniques, as well as remote sensing applications driven by geospatial big data. Besides, two popular concepts, namely, “Social Sensing” and “Urban Computing,” were briefly introduced. Then, we highlighted the role of geospatial big data in mining human activity dynamics and socioeconomic characteristics, and the feasibility of combining with remote sensing data for various studies. Lastly, we presented some empirical case studies on the confluence of remote sensing and geospatial big data in land use extraction, environmental and disaster monitoring, as well as socioeconomic dynamics sensing. The provided examples and discussion demonstrated the high efficiency and complementarity of the integration of remote sensing and geospatial big data, which benefits decision making from multiple perspectives and scales.