Research progress on spatiotemporal data management technologies for natural resources

Abstract

[Objective] Spatiotemporal data management technologies for natural resources have long been a research focus in the field of natural resources. The aim is to clarify the current research status and identify future development trends in this area. [Methods] A structural relational approach is employed to construct an analytical framework centered on natural resource databases. Taking various component technologies as entry points—including natural resource data modeling method, spatiotemporal indexing techniques, and data update method —the content and relationships of key technologies and method for spatiotemporal data management of natural resources are systematically reviewed. Based on the connections between the whole and its parts, future development trends of spatiotemporal data management technologies for natural resources in data application, data modeling, data retrieval, and data updating are analyzed. [Results] At present, technologies for spatiotemporal data management in natural resources are relatively mature in areas such as static entity representation, structured spatiotemporal indexing, homogeneous data updating, and 2 D data storage and sharing. These technologies can effectively support digital data management within a single domain or under a specific modality. However, there are still technical bottlenecks in multi-source data fusion, multimodal model construction, and dynamic data indexing and updating. [Conclusion] To achieve the goals of automated and intelligent data management, future research should focus on the development of multimodal, dynamic, and refined data management technologies.

Keywords

natural resources / natural resource entities / spatiotemporal data / data management / database / influencing factors