(47-1) 15 * << * >> * Russian * English * Content * All Issues

An approach to detecting and eliminating spatial contour artifacts in Web GIS applications
A.V. Vorobev 1,3, G.R. Vorobeva 2,3

Geophysical Center of RAS, 119296, Moscow, Russia, Molodezhnaya St. 3;
Space Research Institute of RAS, 117997, Moscow, Russia, Profsoyuznaya St. 84/32;
Ufa University of Science and Technology, 450076, Ufa, Russia, Z. Validi st. 32

 PDF, 1034 kB

DOI: 10.18287/2412-6179-CO-1127

Pages: 126-136.

Full text of article: Russian language.

Abstract:
One of the common problems of modern geoinformation libraries and interfaces when constructing spatial isolines is the presence of multiple artifacts in the resulting set, in particular, open level lines. As a result, the formed set of spatial isolines after the web rendering procedure makes it difficult to analyze the spatial distribution of the corresponding parameters, on the one hand, and reduces the quality of spatial image rendering, on the other. At the same time, artifacts of spatial isolines are especially critical for large amounts of data. The paper proposes an approach that makes it possible to correct software-generated isolines by identifying open lines and their subsequent selective connection. From the point of view of software implementation, the presented approach practically does not change the response time of server scripts. The effectiveness of the developed approach is confirmed by the example of a web application that provides visualization in the form of a set of spatial isolines of geophysical parameters in the auroral oval region.

Keywords:
spatial isolines, geoinformation technologies, web rendering, geopspatial image artifacts.

Citation:
Vorobev AV, Vorobeva GR. An approach to detecting and eliminating spatial contour artifacts in Web GIS applications. Computer Optics 2023; 47(1): 126-136. DOI: 10.18287/2412-6179-CO-1127.

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

References:

  1. Kurlovich DM. Geoinformation methods of weather analysis and forecasting: study book [In Russian]. Minsk: BGU, 2013.
  2. Yuyukin IV. Spline-interpolation of navigation contours [In Russian]. Vestnik Gosudarstvennogo Universiteta Morskogo i Rechnogo Flota Imeni Admirala S.O. Makarova 2019; 6(58): 1026-1036. DOI: 10.21821/2309-5180-2019-11-6-1026-1036.
  3. Gorokhov S, Shcherbakova T, Sedov S. Elimination of isoline drift when analysis of the electrocardiosignal of the vehicle driver. 2021 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex 2021: 1-5. DOI: 10.1109/TIRVED53476.2021.9639163.
  4. Djavaherpour H, Mahdavi-Amiri A, Samavati F. Static and dynamic progressive geospatial interlinking. ACM Trans Spat Algorithms Syst 2022; 8(2): 16. DOI: 1145/3510025.
  5. Kumari S, Parmar V. GeoWebCln: An intensive cleaning architecture for geospatial metadata. Quaestiones Geographicae 2022; 41(1). DOI: 10.2478/quageo-2022-0004.
  6. Podany J, Stary V, Tomicek J. 3D surface roughness characteristics for biological applications. Manufacturing Technology 2021; 21(6): 836-841. DOI: 10.21062/mft.2021.096.
  7. Pakdil M, Çelik R. Serverless geospatial data processing workflow system design. ISPRS Int J Geoinf 2021; 11(1): 20. DOI: 10.3390/ijgi11010020.
  8. Traxler C, Hesina G. Interacting with big geospatial data. GIM Int 2017; 31: 19-21.
  9. Kachanov P, Zuev A, Yatsenko K. Method of overlapping geospatial data. Bulletin of the National Technical University «KhPI» Series New solutions in modern technologies 2016; 119. DOI: 10.20998/2413-4295.2016.12.17.
  10. Sun K, et al. Geospatial data ontology: the semantic foundation of geospatial data integration and sharing. Big Earth Data 2019; 3(3): 269-296. DOI: 10.1080/20964471.2019.1661662.
  11. Czarnecki J, Jones M. The problem with open geospatial data for on-farm research. Agric Environ Lett 2022; 7(1): e20062. DOI: 10.1002/ael2.20062.
  12. Figueiredo L, [et al.] MoreData: A geospatial data en-richment framework. Int Conf on Advances in Geographic Information Systems 2021: 419-422. DOI: 10.1145/3474717.3484210.
  13. 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.
  14. 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.
  15. 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.

© 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