Benchmarking server-side software modules for handling and processing remote sensing data through Rasdaman

Author(s):  
Athanasios Karmas ◽  
Konstantinos Karantzalos
Author(s):  
E. Kazakov ◽  
A. Terekhov ◽  
E. Kapralov ◽  
E. Panidi

Server-side processing is principal for most of the current Web-based geospatial data processing tools. However, in some cases the client-side geoprocessing may be more convenient and acceptable. This study is dedicated to the development of methodology and techniques of Web services elaboration, which allow the client-side geoprocessing also. The practical objectives of the research are focused on the remote sensing data processing, which are one of the most resource-intensive data types. <br><br> The idea underlying the study is to propose such geoprocessing Web service schema that will be compatible with the current serveroriented Open Geospatial Consortium standard (OGC WPS standard), and additionally will allow to run the processing on the client, transmitting processing tool (executable code) over the network instead of the data. At the same time, the unity of executable code must be preserved, and the transmitted code should be the same to that is used for server-side processing. This unity should provide unconditional identity of the processing results that performed using of any schema. The appropriate services are pointed by the authors as a Hybrid Geoprocessing Web Services (HGWSs). <br><br> The common approaches to architecture and structure of the HGWSs are proposed at the current stage as like as a number of service prototypes. For the testing of selected approaches, the geoportal prototype was implemented, which provides access to created HGWS. Further works are conducted on the formalization of platform independent HGWSs implementation techniques, and on the approaches to conceptualization of theirs safe use and chaining possibilities. <br><br> The proposed schema of HGWSs implementation could become one of the possible solutions for the distributed systems, assuming that the processing servers could play the role of the clients connecting to the service supply server. <br><br> The study was partially supported by Russian Foundation for Basic Research (RFBR), research project No. 13-05-12079 ofi_m.


2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  

2011 ◽  
Vol 17 (6) ◽  
pp. 30-44
Author(s):  
Yu.V. Kostyuchenko ◽  
◽  
M.V. Yushchenko ◽  
I.M. Kopachevskyi ◽  
S. Levynsky ◽  
...  

2017 ◽  
Vol 6 (1) ◽  
pp. 2246-2252 ◽  
Author(s):  
Ajay Roy ◽  
◽  
Anjali Jivani ◽  
Bhuvan Parekh ◽  
◽  
...  

Author(s):  
Rupali Dhal ◽  
D. P. Satapathy

The dynamic aspects of the reservoir which are water spread, suspended sediment distribution and concentration requires regular and periodical mapping and monitoring. Sedimentation in a reservoir affects the capacity of the reservoir by affecting both life and dead storages. The life of a reservoir depends on the rate of siltation. The various aspects and behavior of the reservoir sedimentation, like the process of sedimentation in the reservoir, sources of sediments, measures to check the sediment and limitations of space technology have been discussed in this report. Multi satellite remote sensing data provide information on elevation contours in the form of water spread area. Any reduction in reservoir water spread area at a specified elevation corresponding to the date of satellite data is an indication of sediment deposition. Thus the quality of sediment load that is settled down over a period of time can be determined by evaluating the change in the aerial spread of the reservoir at various elevations. Salandi reservoir project work was completed in 1982 and the same is taken as the year of first impounding. The original gross and live storages capacities were 565 MCM& 556.50 MCM respectively. In SRS CWC (2009), they found that live storage capacity of the Salandi reservoir is 518.61 MCM witnessing a loss of 37.89 MCM (i.e. 6.81%) in a period of 27 years.The data obtained through satellite enables us to study the aspects on various scales and at different stages. This report comprises of the use of satellite to obtain data for the years 2009-2013 through remote sensing in the sedimentation study of Salandi reservoir. After analysis of the satellite data in the present study(2017), it is found that live capacity of the reservoir of the Salandi reservoir in 2017 is 524.19MCM witnessing a loss of 32.31 MCM (i.e. 5.80%)in a period of 35 years. This accounts for live capacity loss of 0.16 % per annum since 1982. The trap efficiencies of this reservoir evaluated by using Brown’s, Brune’s and Gill’s methods are 94.03%, 98.01and 99.94% respectively. Thus, the average trap efficiency of the Salandi Reservoir is obtained as 97.32%.


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