Key Technologies and Applications of Geoscience Big Data

Abstract

Geoscience big data refers to massive, multi-source, heterogeneous, and spatiotemporally associated datasets generated in earth science research and engineering practice. It covers geological, geographical, remote sensing, meteorological, hydrological, environmental, and resource-related data. With the development of remote sensing observation, GIS, Internet of Things monitoring, cloud computing, and artificial intelligence, geoscience data are increasing continuously in scale, type, and update frequency. Conventional stand-alone data processing methods have gradually become insufficient in terms of storage capacity, computing efficiency, spatial query, and integrated analysis. Based on a course report on geoscience big data, this paper reorganizes the concepts, technical framework, implementation process, and application scenario of geoscience big data in the form of an academic paper. First, the data sources, basic characteristics, and processing requirements of geoscience big data are analyzed. Then, a technical framework for geoscience data processing is constructed around cloud servers, Linux operating environments, Hadoop/MapReduce, MongoDB, PostgreSQL/PostGIS, and Python batch processing. On this basis, practical operations, including server connection, file management, MapReduce-based column statistics, and Java program compilation and execution, are summarized. The study indicates that geoscience big data technologies can improve the organization, storage, computation, and analysis efficiency of multi-source geoscience data and provide technical support for typical applications such as WebGIS-based disaster monitoring and early warning. However, further application still needs to address issues such as inconsistent data standards, insufficient data quality control, complex system integration, and data security protection.