UDC: 
DOI: 
10.22389/0016-7126-2022-988-10-9-19
1 Tsvetkov V.Ya.
2 Oznamets V.V.
Year: 
№: 
988
Pages: 
9-19

Moscow State University of Geodesy and Cartography (MIIGAiK)

1, 
2, 
Abstract:
The authors explore the problem of big data in the view of Earth sciences and spatial information. It is noted that the matter has existed since the beginning of space research. The conclusion is that this problem is subtle and is related to the level of developing computer technology and computational models. We analyze the issues of big data occurrence and its reasons. As one of the methods for working with such volumes, cloud computing is proposed. It is proved that spatial data make up the bulk of them. It is shown that streaming information processing methods give way to this technology. The impact of big data on surveying support is researched. The conclusion is made that modern geodetic support can be considered as applied and fundamental. Applied geodetic support is aimed at solving the corresponding problems and is connected with technological supplement. For it the big data issue is important, but not as significant as for fundamental geodetic support. In that level of research it is defined as an integrated set of technologies for which the mentioned matter is relevant. Fundamental geodetic support includes measurements, comprehensive analysis and calculations and is closely related to processing and analysis of big data. As a promising way for it, quantum computing is proposed.
References: 
1.   Andreeva O. A. Geoinformatsionnoe modelirovanie s ispol'zovaniem MLS. Slavyanskii forum, 2019, no. 3 (25), pp. 7–18.
2.   Andreeva O. A. Primenenie mobil'nogo lazernogo skanirovaniya dlya monitoringa ob"ektov transportnoi infrastruktury. Nauka i tekhnologii zheleznykh dorog, 2019, Vol. 3, no. 3 (11), pp. 61–74.
3.   Buravtsev A. V. Tsifrovaya zheleznaya doroga kak slozhnaya organizatsionno-tekhnicheskaya sistema. Nauka i tekhnologii zheleznykh dorog, 2018, Vol. 2, no. 1 (5), pp. 69–79.
4.   Buravtsev A. V., Tsvetkov V. Ya. Oblachnye vychisleniya dlya bol'shikh geoprostranstvennykh dannykh. Informatsiya i kosmos, 2019, no. 3, pp. 110–115.
5.   Kosmicheskie issledovaniya zemnykh resursov: Metody i sredstva izmerenii i obrabotki informatsii (Materialy shkoly-seminara). Moskva: Nauka, 1976, 384 p.
6.   Oznamets V. V. Geodezicheskoe obespechenie mobil'nogo lazernogo skanirovaniya zheleznykh dorog. Nauka i tekhnologii zheleznykh dorog, 2019, Vol. 3, no. 2 (10), pp. 64–76.
7.   Pavlov A. I. Bol'shie dannye v fotogrammetrii i geodezii. Obrazovatel'nye resursy i tekhnologii, 2015, no. 4 (12), pp. 96–100.
8.   Alam S., Albareti F. D., Prieto C. A., Anders F., Anderson S. F., Andrews B. H.,. Armengaud E et al. (2015) The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III. The Astrophysical Journal Supplement Series, no. 219 (1), pp. 1–27.
9.   Bryant R.Е., Katz R.H., Lazowska E.D. Big-data Computing: Creating revolutionary breakthroughs in commerce, science and society.
10.   Chen M., Mao S., Zhang Y., Leung V. C. (2014) Big Data: Related Technologies, Challenges and Future Prospects. Springer, Heidelberg, 100 p. DOI: 10.1007/978-3-319-06245-7.
11.   Chi M., Plaza A., Benediktsson J. A., Sun Z., Shen J., Zhu Y. (2016) Big Data for Remote Sensing: Challenges and Opportunities. Proceedings of the IEEE, no. 104 (11), pp. 2207–2219. DOI: 10.1109/JPROC.2016.2598228.
12.   Corbane C., Pesaresi ћ., Politis P. et al. (2017) Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping. Big Earth Data, no. 1:1-2, pp. 118–144. DOI: 10.1080/20964471.2017.1397899.
13.   Dillon M. (2015) Big universe, big data, astronomical opportunity. URL: www.theguardian.com/science/across-the-universe/2015/jun/25/big-universe-big-data- astronomical-opportunity (accessed: 05.05.2022).
14.   Doukas I. D. (2019) Re-Discovering “Big Data” and “Data Science” in Geodesy and Geomatics. 4th Joint International Symposium on Deformation Monitoring (JISDM). Aristotle University of Thessaloniki, Athens, Greece, No. IKEECONF-2019-447.
15.   Evans M. R., Oliver D., Yang K., Zhou X., Ali R. Y., Shekhar S. (2019) Enabling Spatial Big Data via CyberGIS: Challenges and Opportunities. CyberGIS for Geospatial Discovery and Innovation. GeoJournal Library, no. 118, pp. 143–170.
16.   Fact Sheet: Big Data Across the Federal Government. URL: https://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_fact_sheet_final_1.pdf (accessed: 05.05.2022).
17.   Fortunato M., Ravanelli M., Mazzoni A. (2020) Ionosphere Monitoring with Multi-Frequency and Multi-GNSS Android Smartphone: A Feasibility Study Towards GNSS Big Data Applications for Geosciences. EGU General Assembly Conference Abstracts. DOI: 10.5194/egusphere-egu2020-9450.
18.   Gore A. (1999) The Digital Earth: Understanding our planet in the 21st Century. Photogrammetric Engineering and Remote Sensing, no. 65 (5), pp. 528–530. URL: http://www.digitalearthisde.org/userfiles/The_Digital_Earth_Understanding_our_planet_in_the_21st_Century.doc
19.   Guo H., Wang L., Liang D. (2016) Big Earth Data from Space: A new engine for earth science. Chinese Science Bulletin, no. 61 (7), pp. 505–513. DOI: 10.1007/s11434-016-1041-y.
20.   Guo H., Wang L., Chen F., Liang D. (2014) Scientific Big Data and Digital Earth. Chinese Science Bulletin, no. 59 (35), pp. 5066–5073. DOI: 10.1007/s11434-014-0645-3.
21.   (2009) 2009 Beijing Declaration on Digital Earth. International Journal of Digital Earth, no. 2 (4), pp. 397–399. DOI: 10.1080/17538940903444380.
22.   Li Q., Li D. (2014) Big Data GIS. Geomatics and Information Science of Wuhan University, no. 39 (6), pp. 641–644. DOI: 10.13203/j.whugis20140150.
23.   Li Z., Hu F., Schnase J. L., Duffy D. Q., Lee T. J., Bowen M., Yang C. (2016) A spatiotemporal indexing approach for efficient process of big array-based climate data with MapReduce. International Journal of Geographic Information Science, no. 31 (1), pp. 1–19. DOI: 10.1080/13658816.2015.1131830.
24.   Lynch C. ј. (2008) Big Data: How do your data grow?. Nature, no. 455, pp. 28–29. DOI: 10.1038/455028a.
25.   Müller J., Pail R. (2022) Geodesy 2030. pp. 1–12. DOI: 10.12902/zfv-0392-2022.
26.   Oguntimilehin A., Ademola O. (2014) A Review of Big Data Management, Benefits and Challenges. Journal of Emerging Trends in Computing and Information Sciences, Volume 5, no. 6, pp. 433–438.
27.   Wang C., Zhao Z., Zhang J., Huo J. (2021) Research of Big Data Storage System Based on Underground Space Information. Proceedings of the 2021 ACM International Conference on Intelligent Computing and its Emerging Applications, pp. 234–239. DOI: 10.1145/3491396.3506516.
28.   Xia J., Yang C., Liu K., Li Z., Sun M., Yu M. (2015) Forming a global monitoring mechanism and a spatiotemporal performance model for geospatial services. International Journal of Geographical Information Science, no. 29 (3), pp. 375–396. DOI: 10.1080/13658816.2014.968783.
29.   Yan M., Wu H., Wang L., Huang B., Ranjan R., Zomaya A. Y., Jie W. (2015) Remote sensing Big Data computing: Challenges and Opportunities. Future Generation Computer Systems, no. 51, pp. 47–60. DOI: 10.1016/j.future.2014.10.029.
Citation:
Tsvetkov V.Ya., 
Oznamets V.V., 
(2022) Big data in geodetic software. Geodesy and cartography = Geodezia i Kartografia, 83(10), pp. 9-19. (In Russian). DOI: 10.22389/0016-7126-2022-988-10-9-19