Remote sensing information cloud service: research and practice

Transcription

1 Remote sensing information cloud service: research and practice Yang Banghui Dr., Ren Fuhu Prof. and Wang jinnian Prof

2 Content 1 Background 2 Studying and Designing 3 Practice : Cloud service platform 4 Conclusions

3 Content 1 Background 2 Studying and Designing 3 Practice : Cloud service platform 4 Conclusions

4 Significance Supercomputer era Many People vs. One Computer PC era One Person vs. One Computer Cloud computing era Many People vs. One Computer in Cloud We are here long united, must divide long divided, must unite

5 Significance Space information Application and Service System Powerful Efficient Convenient Economy Massive spatial data Thematic information fusion integrated, in real-time production, secure storage and big data mining efficient access analysis, simulation and prediction of Earth Virtualization spatial data, software, facilities on-demand anytime, and anywhere data store data processing Data and information application

6 Current situation Google launched map platform- Google Earth Builder based on Google cloud technology in April 2013, users can upload an internal map data and by Google Earth and Maps, users can view and manage their own map data. In 2010,ESRI and Amazon cooperated to build a EC2 project based on cloud computing on the Amazon elastic platform, businesses and developers can publish map application, can rapid deploy custom geographic information system in a few minutes.

7 Current situation Other products can be deployed on the EC2 are Spatial Cloud, APOLLO on the Cloud of ERDAS, EarthWhere of GeoEye, Sensor Service Grid (SSG) and so on.

8 Current situation

9 Content 1 Background 2 Studying and Designing 3 Practice : Cloud service platform 4 Conclusions

10 Remote sensing cloud service

11 characteristics Using data, software and equipment through the network at any time without purchasing in advance On-demand and pay-as-you-use services can avoid unnecessary labor and costs Remote sensing data distributed storage and parallel processing capabilities are more powerful, more reliable, and more efficient. Providing users with more choices and combinations of various data, and software according to their actual needs.

12 Key Techniques Remote sensing cloud application Cloud service platform Remote sensing cloud processing Remote sensing cloud store Virtualization Technology parallel computing technology

13 Key Techniques (1) Cloud Store storage strategy and distributed storage management technology of massive remote sensing data. Multi-node, multi-copy secure storage and unlimited dynamic expansion technology of massive remote sensing data. Heterogeneous multi-scale remote sensing data unstructured cloud database management technology. Dynamic publishing, rapid deployment and real-time update technology of massive remote sensing data. Hotspot detection, dynamic server provisioning and high concurrent access optimization technology of massive remote sensing data. 数 据 节 点 1 备 份 节 点 管 理 节 点 遥 感 数 据 块 1 遥 感 数 据 块 2 数 据 节 点 2 遥 感 数 据 块 1 客 户 端 遥 感 数 据 块 2 遥 感 数 据 数 据 节 点 n 遥 感 数 据 块 3 遥 感 数 据 块 4 遥 感 数 据 块 3 遥 感 数 据 块 4 遥 感 数 据 块 n

14 (2) Cloud Processing Processing of remote sensing data parallelization technology based on virtual cloud computing cluster. Multitasking dynamic computing resources on-demand technology of remote sensing data. Remote sensing software algorithms, shared management related resources and online application technology. Remote sensing data processing service interface and secondary development and testing techniques based on cloud environments. Active producing on demand and online interactive technology of remote sensing thematic information products. Key Techniques

15 (3) Cloud Application Key Techniques Platform integration and online sharing technology of remote sensing application resources (data, software, equipment) cloud. Remote sensing applications, resource monitoring, on-demand scheduling and dynamic allocation technology. Safety sharing technology based on remote sensing data in the cloud and virtualized storage environment. Remote sensing network platform, collaborative interpretation, emergency decision support technology based on cloud platform. Accurate remote sensing monitoring and analysis of cloud resources usage. Remote sensing applications for the information society of the mobile terminal services technology. 软 件 资 源 虚 拟 层 资 源 层 虚 拟 机 虚 拟 机 虚 拟 机 虚 拟 机 CPU 内 存 硬 盘 网 络 数 据 资 源

16 The Framework of Platform Application Disaster reduction Land&resources Forestry Service Data & information services Remote sensing software services Platform & processing services Infrastructure services Functition Visual search & browsing based on unified Geo-Space Collabortive data operation & network meeting Software pool management & monitoring Distributed data Storage & Access Remote sensing application systems Data & information online processing & operation Data encryption & authorization Resource database processing software information production line application models networking & facilities

17 Function modules (1) Visual searching and browsing based on unified geo-space (2) Distributed data storage and access (3) Software pool management and monitoring (4) Data and information online processing and operation (5) Remote sensing application systems

18 Function modules (6) Virtual machine dynamic configuration and management (7) Collaborative data operation and network meeting (8) Field data mobile collection and management (9) Data encryption and authorization (10) Account management and utility computing Select data and software Using Virtual machine via the internet

19 Content 1 Background 2 Studying and Designing 3 Practice : Cloud service platform 4 Conclusions

20 Remote sensing cloud service platform We have developed a remote sensing cloud service platform--- rscloudmart. Integrated cloud services of remote sensing data, processing software and computing facilities are achieved through this prototype. Now, this platform officially launched on December 11,2014. Information product Cloud workbench Application collection Custom service Data center RS community

21 Data center Massive remote sensing data resources online preview, leasing, processing and purchase service. Data service partners a. Planet Labs(USA) Update the national satellite image data every week b. Chinese satellites Provide the domestic high resolution satellite image data c. Various third party data provider

22 Information product Remote sensing information collection, product lease, specialty shop, online preview, retrieving and purchasing Remote Sensing Mart

23 Cloud workbench Online professional RS workbench Low cost Leasing according to the need Integration Virtual machine + software + data High security level Private cloud environment

24 Custom services Provide all industries (agriculture,forest,land use,water,environment)thematic monitoring and integration information service report and equal standard service product and solution,provide thematic monitoring,information product production custom services.

25 Application collection Gather industry thematic RS services platform Provide data and application development API information product production application platforms equipped and so on, encourage developers back to innovation.

26 Remote Sensing community

27 Content 1 Background 2 Studying and Designing 3 Practice : Cloud service platform 4 Conclusions

28 Conclusions 1 Remote sensing cloud services are the best way to improve popularization and application of remote sensing business. 2 Remote sensing cloud services are an effective solution to the problem of spatial data and software sharing. 3 Remote sensing cloud services are a new engine to promote the development of the industry. 4 Remote sensing cloud service is an efficient platform for technological innovation.

29 WEBSITE

30