Abstract: Based on the limitations of existing research on geographic information service technology,which is only limited to the new software deployment of existing GIS software in the cloud computing environment and the application mode of a single field,this paper proposed that the research on geographic information service technology needs to carry out in-depth research on the application mode in the cloud computing environment and deepen the fusion application of geospatial and temporal big data characterized by earth observation information.Furthermore,it analyzed the technical characteristics of cloud GIS service platform in five aspects:data management,geographic computing,geographic information mapping and terminal service,thematic application system construction,network application and service mode.By referring to and classifying and dialectically analyzing the classic theoretical and empirical literatures in this field at home and abroad,in line with the latest trend of cloud GIS research,the technical framework of cloud GIS platform integrating “store-computing-service” was designed,and five models of geographic information service based on cloud computing were proposed according to the current application status of cloud GIS platform.MongoDB was adopted as the carrier of information and business data and GridFS file system was applied as a kind of underlying heterogeneous storage.At the same time,Redis was utilized as the data exchange cache of the data engine to ensure the processing efficiency,and ZeroMQ was leveraged as the transmission middleware to develop the data engine and resource service of the “Mannger-worker” mode based on Node.js.Aiming at the problem of storage and management of massive,high-throughput,spatially-structured remote sensing image data and its basic land information products,a prototype system was developed based on MongoDB database and tested with PB data,which verified the feasibility and advancement of the research results in this paper.

Key words: Cloud computing, Geographical spatitemporal data, Service mode