Geospatial Big Data: Survey and Challenges
Jiayang Wu, Wensheng Gan, Han-Chieh Chao, Philip S. Yu
TL;DR
This paper surveys geospatial big data (GBD) and its mining, highlighting diverse data sources from satellites, sensors, and mobile devices, and the integration of AI technologies within a unified processing framework. It details a comprehensive pipeline—from data collection and storage to retrieval, analysis, prediction, and visualization—while examining emergent tools such as large language models, knowledge graphs, and the Metaverse to enhance GBD utility. The authors illustrate concrete applications in urban management and environmental sustainability, and address practical challenges including data retrieval performance and privacy, offering concrete directions like vectorized GBD databases, heterogeneous data management, and privacy-preserving techniques. Overall, the work emphasizes how a tightly integrated, AI-enabled GBD framework can transform city planning, resource management, and environmental monitoring, with implications for scalability, security, and semantic interoperability across data sources.
Abstract
In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big earth observation data and big human behavior data. Identifying geospatial patterns from GBD has been a vital research focus in the fields of urban management and environmental sustainability. This paper reviews the evolution of GBD mining and its integration with advanced artificial intelligence (AI) techniques. GBD consists of data generated by satellites, sensors, mobile devices, and geographical information systems, and we categorize geospatial data based on different perspectives. We outline the process of GBD mining and demonstrate how it can be incorporated into a unified framework. Additionally, we explore new technologies like large language models (LLM), the Metaverse, and knowledge graphs, and how they could make GBD even more useful. We also share examples of GBD helping with city management and protecting the environment. Finally, we discuss the real challenges that come up when working with GBD, such as issues with data retrieval and security. Our goal is to give readers a clear view of where GBD mining stands today and where it might go next.