Geospatial Data Storage and Management

Related Works

Breunig et al. (2020) highlighted the indispensable role of geospatial data management in addressing contemporary challenges in big data applications, such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning. Albrecht et al. (2020) examined the transformative impact of next-generation geospatial-temporal information technologies on disaster management, emphasizing the utilization of scalable technologies such as Apache Hadoop Distributed File Systems and NoSQL key-value stores to efficiently process and analyze large volumes of geocoded data for early warning, impact assessment, and response to natural disasters.

Li (2020) outlined the significance of high-performance computing (HPC) in addressing the challenges posed by geospatial big data, emphasizing its crucial role in efficiently collecting, managing, storing, and analyzing massive datasets collected from ubiquitous location-aware sensors. Dangermond and Goodchild (2020) discussed the evolution of visions for geospatial technology. They emphasized the necessity of a new vision centered around geospatial infrastructure to address challenges such as open access, sharing, engagement, and integrating emerging technologies like Big Data, artificial intelligence, and data science.