A Study of Distributed Remote Sensing Data Sharing Platforms Based on Web Services

Author(s):  
Fan Li ◽  
Xu Zhang ◽  
Hongrong Wang ◽  
Yong Shan
Author(s):  
Xuan Ma ◽  
Zhibao Wang ◽  
Lu Bai ◽  
Bingbing Xu ◽  
Juntao Gao ◽  
...  

2019 ◽  
Vol 75 ◽  
pp. 03001
Author(s):  
Alexey Buchnev ◽  
Pavel Kim ◽  
Valery Pyatkin ◽  
Fedor Pyatkin ◽  
Evgeny Rusin

We consider a distributed network of cloud web services for processing satellite data, which provides data processing facilities for Earth remote sensing within SaaS model. In fact, this is a set of web services that implement the functional modules of the PlanetaMonitoring remote sensing data processing system.


2021 ◽  
Vol 4 (1) ◽  
pp. 66-71
Author(s):  
Aleksey A. Buchnev ◽  
Valery P. Pyatkin ◽  
Evgeny V. Rusin

The organization of computations in cloud Web services for satellite data processing is considered. Computing component of almost every service is a batch version of the corresponding technology of the PlanetaMonitoring software for processing remote sensing data. The exceptions are the technologies that require interactive communication with user, i.e., supervised classification of remote sensing data and movement tracking of natural environments by the coordinates of identifiable objects, each of which consists of two parts, i.e., an interactive Windows application running on the user's computer and the part hidden in the cloud.


Author(s):  
Chunyang Hu ◽  
Yongwang Zhao ◽  
Dianfu Ma

Satellite remote sensing imagery data is an important Geospatial data which is playing an increasingly important role in many applications such as crisis management, military activities and government decision-making. However, it will continue to be a great challenge to organize and manage these multi-dimension massive remote sensing data for collaborative visualization services in Internet environment. In this chapter the authors proposed a global hierarchical data model of massive multi-dimension remote sensing data based on tiling and pyramid technologies for the organization and management of multi-source and multi-scale remote sensing data. The authors implemented a collaborative Geospatial data visualization system based on their proposed storage structure of data model using Web Services, WSRF and Web2.0 technologies. Finally, the authors evaluated the prototype system with real data sets, which demonstrated the high performance data visualization in their system.


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