ALL-RUSSIA OPEN ANNUAL CONFERENCES ON
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE
Principal physics, methods and techniques for monitoring the environment, potentially dangerous phenomena and objects
рус
Proceedings of the 16th Conference (November 12-16, 2018, Moscow, Russia)
Organization of Functioning of the Space Monitoring System of Emercom of Russia Regarding Using Information Technologies of Distributed Storage and Multithreaded Data Processing to Improve Quality and Efficiency of Decision Making in Forecasting and Eliminating Emergency Situations
Yaroslav V. Alekseenko1,2
- Russian Agency for Disaster and Emergency Management, Moscow, Russia
alex.zik@mail.ru
- Saint-Petersburg University of State Fire Service of EMERCOM of Russia, Saint-Petersburg, Russia
DOI 10.21046/rorse2018.335
Emercom of Russia actively applies results of remote sensing to ensuring protection of the population and territories from emergency situations. The observed growth of volume of the obtained data demands to provide guaranteed data storage and also to provide operational data processing. During the forecasting and emergency response the population, objects of economy and the environment need to take in the shortest possible time measures for prevention of loss of human life and causing material damage. It it can be reached only by reduction of time from the moment of detection of threat prior to holding necessary actions. For a solution of this problem in article the model of creation of a distributed file system and the distributed multithreaded data processing received by the System of space monitoring of Emercom of Russia is offered. The offered model allows to provide guaranteed data storage, to reduce time for data processing and also to provide flexible scaling of hardware.
Keywords: data processing, data storage, processing of space pictures, the distributed data storage, multithreaded processing, the system of space monitoring, Emercom of Russia
References: - [1] Rozenberg I.N. Space Monitoring. Slavyanskii forum, 2016, № 2, pp. 216-222. (In Russian).
- [2] Toth C., Jóźków G. Remote Sensing Platforms and Sensors: A Survey. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, Т. 115, pp. 22-36. DOI: 10.1016/j.isprsjprs.2015.10.004.
- [3] Giri C. P. Remote Sensing of Land Use and Land Cover: Principles and Applications. CRC Press, 2016. DOI: 10.1201/b11964.
- [4] Fakhmi Sh.S., Kryukova M.S., Alekseenko Ya.V., Salem Ali, Video System of Space Monitoring of Emercom of Russia for Managerial Decision Making. Technologies for Constructing Transport systems, 2018, pp. 236-243. (In Russian).
- [5] Yu.I. Shokin et al. An information system for acquisition, processing and access to satellite data and its applications in environmental monitoring. Computational Technologies, 2015, 20 (5). (In Russian).
- [6] Kolesenkov A. N. Modern Approaches to the Processing of Data in the Construction of Information Systems of Environmental Monitoring. Izvestiya Tula State University, 2016, №9. (In Russian).
- [7] Hoque M. A. A. et al. Tropical cyclone disaster management using remote sensing and spatial analysis: A review. International journal of disaster risk reduction, 2017, Т. 22, pp. 345-354. DOI: 10.1016/j.ijdrr.2017.02.008.
- [8] Liu Y., Wu L. Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning. Procedia Computer Science, 2016, Т. 91, pp. 566-575. DOI: 10.1016/j.procs.2016.07.144.
- [9] Fakhmi Sh.S., Almahrouk Muhib Muhamed, Kryukova M.S., Alekseenko Ya.V. Model of Distributed Processing and Storage of Space Pictures. Technologies for Constructing Transport systems, 2018, pp. 231-236. (In Russian).
- [10] Shvachko K. V. et al. Distributed File System Using Consensus Nodes : patent. 9424272 USA, 2016.
- [11] Ramakrishnan R. et al. Azure Data Lake Store: a Hyperscale Distributed File Service for Big Data Analytics. Proceedings of the 2017 ACM International Conference on Management of Data. – ACM, 2017, pp. 51-63. DOI: 10.1145/3035918.3056100.
- [12] Kakoulli E., Herodotou H. OctopusFS: A Distributed File System With Tiered Storage Management. Proceedings of the 2017 ACM International Conference on Management of Data. – ACM, 2017, pp. 65-78. DOI: 10.1145/3035918.3064023.
- [13] Sherstnev V. S. et al. Development of Distributed File System for Storing Weather Data. 22nd International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics. – International Society for Optics and Photonics, 2016, Т. 10035, pp. 100350G. DOI: 10.1117/12.2249248.
- [14] Mori K. I. et al. Design of a Local Parallel Pattern Processor for Image Processing. Special Computer Architectures for Pattern Processing. – CRC Press, 2018, pp. 197-210. DOI: 10.1109/AFIPS.1978.71.
- [15] Ma Y. et al. Parallel Programing Templates for Remote Sensing Image Processing on GPU Architectures: Design and Implementation. Computing, 2016, Т. 98, №. 1-2, pp. 7-33. DOI: 10.1007/s00607-014-0392-y.
- [16] Tan K. et al. GPU Parallel Implementation of Support Vector Machines for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, Т. 8, №. 10, pp. 4647-4656. DOI: 10.1109/JSTARS.2015.2453411.
- [17] Li W. et al. GPU Parallel Implementation of Isometric Mapping for Hyperspectral Classification. IEEE Geoscience and Remote Sensing Letters, 2017, Т. 14, №. 9, pp. 1532-1536. DOI: 10.1109/LGRS.2017.2720778.
- [18] Rathore M. M. U. et al. Real-Time Big Data Analytical Architecture for Remote Sensing Application. IEEE journal of selected topics in applied earth observations and remote sensing, 2015, Т. 8, № 10, pp. 4610-4621. DOI: 10.1109/JSTARS.2015.2424683.
Download pdf
Section 6. Information systems for working with remote monitoring data
335-342