سابِق|تال

Research and application of PIE-Engine Studio for spatiotemporal remote sensing cloud computing platform

  • role： First author第一作者
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

  • Email：chengwei@piesat.cn
  • Introduction：程伟，1987年生，女，博士，研究方向为环境变化监测。E-mail： chengwei@piesat.cn
CHENG Wei,  
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

QIAN Xiaoming,  
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

LI Shiwei,  
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

MA Haibo,  
  • role： Corresponding author通信作者
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

  • Email：liudongsheng@piesat.cn
  • Introduction：刘东升，1984年生，男，高级工程师，研究方向为遥感图像处理与应用。E-mail： liudongsheng@piesat.cn
LIU Dongsheng*,  
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

LIU Fuqian,  
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

LIANG Junlong,  
  • Affiliation：

    Piesat Information Technology Co., Ltd., Beijing 100195, China

HU Ju

ملخص

With the arrival of remote sensing big data era, numerous remote sensing cloud computing platforms have emerged inland and overseas to rapidly process and analyze massive remote sensing data. The emergence of remote sensing cloud computing platform makes it possible to quickly analyze and apply remote sensing data on a global scale or for longterm sequences. However, currently, there is lacking of remote sensing cloud computing platform with complete functions in domestic, while foreign remote sensing cloud computing platform has insufficient support for domestic satellite data. Based on this situation, we have independently developed a spatiotemporal remote sensing cloud computing platform, PIE (Pixel Information Expert) -Engine Studio. By adopting container cloud technology, this platform integrating data, computing power and technology, can implements on-demand acquisition of remote sensing data and rapid processing of massive data just driven by the script. (1) This study first introduced the system architecture of PIE-Engine Studio, and then described the data storage and access mode. (2) PIE-Engine Studio provides operations for multiple objects such as number, matrix, image, vector, list, dictionary, etc., also machine learning algorithms and some special satellite algorithms. (3) Furthermore, this study illustrated the calculation flow of the platform in detail. Firstly, the user writes a script in the front-end to describe the calculation process of remote sensing data. Click the “Run” button, these codes automatically build the preliminary chained structure call syntax tree. Then the syntax tree is optimized in the back-end through filter the invalid calculation content. The computing tasks are then distributed to the computing services on multiple nodes through the scheduling center. Finally, the resulting visual map layer or data file is returned to the front-end interface triggered by specific front-end requests or operators (print, addLayer, export).(4) At last, an application case is presented, we adopted Landsat 8 data and taking the calculation of Normalized Difference Vegetation Index (NDVI) in the growing season as an example, the calculation results and running time of this platform are compared with Google Earth Engine (GEE). The results show that, due to the limitation of computing resources, the running and export time of this platform are slightly longer than that of GEE, but the spatial distribution of calculation results is consistent, among which about 68% values are distributed between (0.48, 0.77), and 95.33% of the difference between the two results is concentrated between (-0.13, 0.13). It shows that the results are reliable. Therefore, the remote sensing cloud computing platform constructed by this paper, can provide data resources and computing power for research in the field of earth science, and will help promote the development of remote sensing cloud computing platform in China and the application of domestic satellite data in cloud computing platform.

مفهوم

remote sensing;big data;remote sensing cloud computing platform;distributed storage;parallel computing

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