A novel design and application of spatial data management platform for natural resources

Abstract

Owing to the continuous development of earth observation technology in recent years, substantial amount of remote sensing image and natural resources business data have been generated. However, the traditional method of managing remote sensing image and natural resources data can no longer be used for organizing and managing big data. Along with the increasing requirements of data services, methods to efficiently organize large-scale remote sensing image and natural resource data are currently a hot topic With this background, we herein combine the Hadoop framework with the GIS technology to build a set of independent frameworks for the distributed data managing and service querying in order to enable the quick access and mass storage of large volumes of remote sensing image and natural resource data and to resolve the problem of big-data management. Additionally, to effectively improve the efficiency of query and service of remote sensing images and natural resource data, we use the services, such as those of land approval, regional planning application, and ground traffic analysis, to optimize the data storage model. Then we apply the model to solve the technical problems of large-scale data service release in a distributed computing environment. The results shows that, in terms of data management, the designed platform changed the traditional folder-type or common file-type system storage mode and solved the difficult problem of managing and applying massive remote sensing images and natural resources data. In terms of the query efficiency, compared with the single-node computing mode, the query efficiency of the platform for approved but not provided for use lands increased by 98.9%Tthe statistical efficiency of project-compliant land increased by 51.3%, and the analysis efficiency of land flow increased by 75.2%, and the data query service efficiency significantly improved. This study provides a solution to achieve the effective application of large-scale data and, in particular, contributes toward the application of remote sensing images and natural resource data.