(48-1) 15 * << * >> * Russian * English * Content * All Issues

An approach to dynamic visualization of heterogeneous geospatial vector images
A.V. Vorobev 1,2,3, G.R. Vorobeva 2

1 Geophysical Center of RAS,
119296, Moscow, Russia, Molodezhnaya St. 3;
2 Ufa University of Science and Technology,
450076, Ufa, Russia, Z. Validi st. 32;
3 Ufa State Petroleum Technological University,
450064, Ufa, Russia, Kosmonavtov st. 1

 PDF, 1279 kB

DOI: 10.18287/2412-6179-CO-1279

Pages: 123-138.

Full text of article: Russian language.

Abstract:
One of the well-known problems of geoinformation software libraries in the visualization of geospatial data is the low efficiency and limited frame-by-frame change of a group of spatial layers with time reference. Among the most significant challenges of visualization is that the synchronous switching of a group of spatial layers is impossible to perform, which leads to less accurate estimates of the temporal anisotropy of the corresponding spatial data. The problem is further exacerbated by the heterogeneity of spatial information, which is expressed in sampling steps, formats used by geospatial primitives. The said problem significantly complicates the analysis of spatiotemporal information in many research and application areas. An illustrative example may be found in the problem of analyzing the spatiotemporal anisotropy of geophysical information. Here, for the visualization to be implemented, the dynamic evaluation of the retrospective data in a given time interval needs to performed. The paper proposes an approach that enables heterogeneous vector geospatial data to be integrated before subsequent processing, analysis and visualization. The effectiveness of the developed approach is confirmed by the example of a web application for visualizing a geospatial image as an array of spatial polylines of arbitrary data, as well as in the problem of analysis of spatiotemporal variations of geophysical data.

Keywords:
spatial data, geoinformation technologies, geospatial image, geospatial primitives.

Citation:
Vorobev AV, Vorobeva GR. An approach to dynamic visualization of heterogeneous geospatial vector images. Computer Optics 2024; 48(1): 123-138. DOI: 10.18287/2412-6179-CO-1279.

Acknowledgements:
The work was funded by the Russian Science Foundation under project No. 21-77-30010.

References:
  1. Breunig M, et al. Geospatial data management research: progress and future directions. ISPRS Int J Geoinf 2020; 9: 95. DOI: 10.3390/ijgi9020095.
  2. Ma X. Linked Geoscience Data in practice: where W3C standards meet domain knowledge, data visualization and OGC standards. Earth Sci Inform 2017; 10: 429-441. DOI: 10.1007/s12145-017-0304-8.
  3. Zhu X, et al. Development and implementation of a dynamic and 4D GIS based on semantic location model. Geo Spat Inf Sci 2019; 22: 193-213. DOI: 10.1080/10095020.2019.1649192.
  4. Sergiu D, Suletea A, Botez A, Jandic T. The use of spatial modeling to teach engineering graphics. J Soc Sci 2022; 5: 104-112. DOI: 10.52326/jss.utm.2022.5(2).09.
  5. Chen F, Ma X, Ren H. Progressive transmission of vector spatial data. J Netw 2013; 8(5): 1065-1072. DOI: 10.4304/jnw.8.5.1065-1072.
  6. Zhang H, Gao P, Li Zh. Improvements to information entropy for raster spatial data: A thermodynamic-based evaluation. Abstracts of the ICA 2019; 1: 426. DOI: 10.5194/ica-abs-1-426-2019.
  7. Alqradaghi K, et al. Geospatial technique integrated with MC. Water Air and Soil Pollution 2022; 233: 313. DOI: 10.1007/s11270-022-05796-2.
  8. Yu B, Zhang C, Sun J, Zhang Yu. Massive GIS spatio-temporal data storage method in cloud environment. CSAI '18: Proc 2018 2nd Int Conf on Computer Science and Artificial Intelligence 2018: 105-109. DOI: 10.1145/3297156.3297193.
  9. Al-Mahfoodh N, Marzuki I, Samsuri A. Spatio-temporal modelling of noise pollution. Int J Integr Eng 2021; 13: 125-131. DOI: 10.30880/ijie.2021.13.03.015.
  10. Peuquet D, Duan N. An event-based spatio-temporal data model (ESTDM) for temporal analysis of geographical data. Int J Geogr Inf Sci 1995; 9(1): 7-24.
  11. Beiler M, Filion E. amtrak rail trespasser analysis using a GIS space-time approach. Public Work Manag Policy 2021; 26: 1087724X2110032. DOI: 10.117.7/1087724X211003249.
  12. Rachmatullah R, Azizah N. A conceptual data model for flood based on cellular automata using moving object data model. J Phys: Conf Ser 2017; 801: 012037. DOI: 10.1088/1742-6596/801/1/012037.
  13. Moins I, Boggio D. SIRS Digues 2.0: A cooperative software for levees management. E3S Web of Conferences 2016; 7: 04018. DOI: 10.1051/e3sconf/20160704018.
  14. Battista A, Saquicela V, Vilches-Blázquez L, et al. Geographical linked data: A Spanish use case. Proc 6th Int Conf on Semantic Systems (I-SEMANTICS '10) 2010: 36. DOI: 10.1145/1839707.1839753.
  15. Egiazarian V, et al. Deep vectorization of technical drawings. In Book: Vedaldi A, Bischof H, Brox T, Frahm J-M, eds. Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIII. Berlin, Heidelberg: Springer-Verlag; 2020. DOI: 10.1007/978-3-030-58601-0_35.
  16. Kotsakis C. Spatial coordinate transformations with noisy data. In Book: Pepe A, Zhao Q, eds. Geospatial analyses of earth observation (EO) data. Ch 7. InTech Open; 2019. DOI: 10.5772/intechopen.87846.
  17. Sekulovic D, Basaric M, Garic I. Helmert transformation on the case of Gauss-Krüger and UTM. Contemporary Theory and Practice in Construction 2019; 14: 288-298. DOI: 10.7251/STP2014288S.
  18. Diakite A, Zlatanova S. Automatic geo-referencing of BIM in GIS environments using building footprints. Comput Environ Urban Syst 2020; 80: 101453. DOI: 10.1016/j.compenvurbsys.2019.101453.
  19. Ding H, et al. RTVEMVS: Real-time modeling and visualization system for vehicle emissions on an urban road network. J Clean Prod 2021; 309: 127166. DOI: 10.1016/j.jclepro.2021.127166.
  20. Horbiński T, Lorek D. The use of Leaflet and GeoJSON files for creating the interactive web map of the preindustrial state of the natural environment. J Spat Sci 2020: 67(1): 61-77. DOI: 10.1080/14498596.2020.1713237.
  21. Vorobev AV, et al. Short-term forecast of the auroral oval position on the basis of the “virtual globe” technology. Russ J Earth Sci 2020; 20: ES6001. DOI: 10.2205/2020ES000721.
  22. Vorobev AV, et al. System for dynamic visualization of geomagnetic disturbances based on data from ground-based magnetic stations. Sci Vis 2021; 13(1): 162-176. DOI: 10.26583/sv.13.1.11.
  23. Vorobev AV, Pilipenko VA, Enikeev TA, Vorobeva GR. Geographic information system for analyzing the dynamics of extreme geomagnetic disturbances based on observation data from ground stations. Computer Optics 2020; 44(5): 782-790. DOI: 10.18287/2412-6179-CO-707.
  24. Nguyen LAT, et al. Design and implementation of web application based on MVC Laravel architecture. European Journal of Electrical Engineering and Computer Science 2022; 6: 23-29. DOI: 10.24018/ejece.2022.6.4.448.
  25. Marculescu B, Zhang M, Arcuri A. On the faults found in REST APIs by automated test generation. ACM Trans Softw Eng Methodol 2022; 31(3): 41. DOI: 10.1145/3491038.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20