Tracking Changes in Mining Object Topology on Rectangular and Hexagonal Grids

Full Text:


The Earth remote sensing technologies (ERS) in exploration largely determine their effectiveness. Therefore, development of a new methodological support for the use of remote sensing data in predicting mining and geological conditions is a key priority area. The studies are based on the analysis of the assessment of information, social, and economic efficiency of remote sensing data application at mineral deposits. The role of application of new technologies (including remote ones) in the process of optimizing initial exploration stages is noted. The possibilities of using remote sensing data to assess general nature, direction, and extent of environmental changes due to mining activities are shown. A technique is proposed that can be used in the process of tracking changes in the topology of objects in the course of mining. The differences in the results obtained using the proposed technique for processing satellite images on rectangular and hexagonal grids (rasters) are considered. The advantages of using the hexagonal grid for tracking the boundaries of objects and formation of signs are shown. Practical examples - a number of open source satellite images processed using the proposed method - are presented. The study findings allow applying intelligent analysis of satellite imagery data with the subsequent identification of the earth's surface objects of interest. An example of using the obtained results together with specialized software tools (such as GIS INTEGRO geographic information system capable of solving geological problems, or the foreign ArcGIS system) for constructing contour maps of the territory and obtaining its description based on topological relations and metric information is shown.

About the Authors

S. O. Kramarov
Surgut State University
Russian Federation

O. Yu. Mityasova
Surgut State University
Russian Federation


1. Kramarov S. O., Hramov V. V., Mityasova O. Yu. Satellite Identification of Mining Objects at Deposits Developed by Open-Cut Method. Mining Information and Analytical Bulletin. 2019;(5):72-79. DOI: 10.25018/0236-1493-2019-05-0-72-79. (In Russ.).

2. Ivanov V.A., Smirnov V.A. Geoinformation systems, general course. Stavropol; 2000. (In Russ.).

3. Gandhi S. M., Sarkar B. C. Essentials of Mineral Exploration and Evaluation. Elsevier, Amsterdam; 2016. 410 p. DOI: 10.1016/C2015 -0-04648-2.

4. Fundamentals of Topology [electronic source]. Creation and publication of maps, analytics and data. Available from: [Accessed: 28.01.2020]. (In Russ.).

5. Haldar S. K. Mineral Exploration. Principles and Applications. Elsevier, Amsterdam; 2018. 378 p. DOI: 10.1016/C2017-0-00902-3.

6. Bobrowsky P. T., Marker B. Encyclopedia of Engineering Geology. Springer, Cham; 2018. 978 p. DOI: 10.1007/978-3-319-12127-7.

7. Kramarov S.O., Hramov V.V., Mityasova O. Yu., Groshev A.R. A method for contour coding of geoinformation space object models on hexagonal grids based on remote sensing data. Modern problems of remote sensing of the earth from space: thesis. doc. Vseros. conf. Moscow, November 11-15, 2019. Moscow; 2019. P. 40. (In Russ.).

8. Middleton L., Sivaswamy J. The FFT in a hexagonal image processing framework. Proceedings of Image and Vision Computing; 2001. P. 231-236.

9. Wu H. S. Hexagonal discrete cosine transform for image coding. Electronics Letters. 1991;27(9):781-783.

10. Revuelta M. B. Mineral Resources. From Exploration to Sustainability Assessment. Springer, Cham; 2019. 653 p. DOI: 10.1007/978-3-319-58760-8.

11. Verkhozin S. S. Remote Sensing in Intelligence and Other Areas. Zolotodobycha. 2018;4(233):36-39. (In Russ.).

12. Roonwal G. S. Mineral Exploration: Practical Application. Springer, Singapore; 2018. 298 p. DOI: 10.1007/978-981-10-5604-8.

13. Geology and Mining [electronic source]. GIS technologies: integration of geographic information systems (GIS) - Sovzond. Available from: [Accessed 10.02.2020]. (In Russ.).

14. Schowengerdt Robert A. Remote Sensing: Models and Methods for Image Processing. 3rd Edition, eBook. Academic Press; 2006. 560 p.

15. GIS INTEGRO. Geoinformation technologies for nature management [electronic source]. Geoinformatics Department of FSBI "VNIGNI". Moscow, VNIGNI Publ.; 2018-2020. Available from: [Accessed 24.09.2019]. (In Russ.).

16. Falsaperla S., Hammer C., Langer H. Advantages and Pitfalls of Pattern Recognition Selected Cases in Geophysics. Amsterdam: Elsevier; 2020. 330 p.

17. Marjoribanks R. Geological Methods in Mineral Exploration and Mining. Springer, Berlin, Heidelberg; 2010. 248 p. DOI: 10.1007/978-3-540-74375-0.

18. Bartalaev S. A., Egorov V. A, Zharko V. O. et al. Satellite mapping of vegetation cover of Russia. Moscow, IKI RAS Publ.; 2016. 208 p. (In Russ.).

19. Hexagonal Grids. Red Blob Games from Amit Patel. Available from: [Accessed 13.02.2020].

20. Kramarov S. O., Groshev A. R., Karataev A. S. et al. Possibilities of automation of contour recognition and identification of the earth's surface objects. In: Modern problems of remote sensing of the earth from space: thesis. doc. Vseros. conf. Moscow, November 12-16, 2018. Moscow; 2018. p. 414. (In Russ.).

21. Hramov V. V., Gvozdev D. S. Intelligent information systems. Textbook. Rostov, Rostov State Transport University Publ.; 2012. 134 p. (In Russ.).

22. Nagy B. Shortest Paths in Triangular Grids with Neighbourhood Sequences. Journal of Computing and Information Technology. 2003; 11(2): 111-122. DOI: 10.2498/cit.2003.02.04.

23. Panov R. S. Development of exploration - the key to stable economic development of Russia. Analytical Bulletin. 2014;16(534):7-16. (In Russ.).

24. Hofmann P., Tiede D. Image segmentation based on hexagonal sampling grids. South-Eastern European Journal of Earth Observation and Geomatics. 2014;3(2S):173-177.

25. On the approval of Development Strategy for Mineral Resources Base of the Russian Federation up to 2035. Official Internet portal of legal information. Available from: [Accessed 14.02.2020]. (In Russ.).

26. Temkin I. O., Goncharenko A. N. Problems of modeling the interaction of intelligent agents at a mining enterprise. St. Petersburg State Polytechnical University Journal. 2014;4-2(183):252-259. (In Russ.).

Supplementary files

For citation: Kramarov S.O., Mityasova O.Y. Tracking Changes in Mining Object Topology on Rectangular and Hexagonal Grids. Gornye nauki i tekhnologii = Mining Science and Technology (Russia). 2020;5(2):154-161.

Views: 213


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

ISSN 2500-0632 (Online)