1. Loginov D.S. Kartograficheskoe obespechenie kak process: konceptual'naya model' sistemy i ee primenenie [Cartographic support as a process: conceptual model of system and its application]. Geodesy and Cartography. 2026;2: 10–22. (In Russian). DOIhttps://doi.org/10.22389/0016-7126-2026-1028-2-10-22.

2. Nagovicyn O.V., Lukichev S.V. Istoriya razvitiya otechestvennyh gorno-geologicheskih informacionnyh sistem [History of development of the Russian mining and geological information systems]. Russian Mining Industry. 2024;(5): 46–51. (In Russian). DOI:https://doi.org/10.30686/1609-9192-2024-5-46-51.

3. Shpil'man A.V., Chikisheva A.V. Geoinformacionnye tehnologii dlya monitoringa nedropol'zovaniya [Information technology for subsurface monitoring]. Subsoil Use XXI Century. 2016;2 (69): 78–83. (In Russian).

4. FGIS «Edinyj fond geologicheskoj informacii o nedrah» kak osnova cifrovoj transformacii nedropol'zovaniya [Unified subsurface geological information fund “Federal State Information System”: basis for subsoil use digital transformation] / D.B. Arakcheev, E.M. Yuon, I.V. Zaharkin, S.G. Shahnazarov. Oil and gas geology. 2021;3: 21–29. (In Russian). DOIhttps://doi.org/10.31087/0016-7894-2021-3-21-29.

5. GIS INTEGRO. Sostoyanie i perspektivy razvitiya v usloviyah importozameshcheniya [GIS INTEGRO. Status and prospects for development in the context of import substitution] / E.N. Cheremisina, M.Ya. Finkel'shtejn, K.V. Deev, E.M. Bol'shakov. Oil and gas geology. 2021;3: 31–40. (In Russian). DOIhttps://doi.org/10.31087/0016-7894-2021-3-31-40.

6. Meindl B, Mendonca J. Mapping Industry 4.0 Technologies: From Cyber-Physical Systems to Artificial Intelligence. 2021.Available from: https://arxiv.org/abs/2111.14168 (Accessed 07 June 2026).

7. Digital twins in the minerals industry – a comprehensive review / J. Qu, M.S. Kizil, M. Yahyaei, P. F. Knights // Mining Technology: Transactions of the Institute of Mining and Metallurgy. 2023;132(4): 267–289.

8. Zhironkina O.V., Zhironkin S.A., Cehlar M. et al. Technological and Intellectual Transition to Mining 4.0 // Energies. 2023;16(3). Article 1427. DOI:https://doi.org/10.3390/en16031427.

9. Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges / Li S., Dragicevic S., Anton F., et al. // ISPRS Journal of Photogrammetry and Remote Sensing. 2016. Available from: https://arxiv.org/abs/1511.03010 (Accessed 07 June 2026).

10. Ang M. L. E., Bottrell S. H. et al. Systematic Review of GIS and Remote Sensing Applications for Assessing the Socioeconomic Impacts of Mining // Environmental Management. 2023. DOIhttps://doi.org/10.1177/10704965231190126.

11. Mohamad A.A., Ujang U., Azri S., et al. Enhancing 3D geospatial modelling through multimodal data and machine learning: a systematic literature review. Spatial Information Research. 2026;34(12). DOIhttps://doi.org/10.1007/s41324-025-00659-4.

12. Lindi O.T., Aladejare A.E., Ozoji T.M., et al. Uncertainty Quantification in Mineral Resource Estimation. Natural Resources Research. 2024;33:2503–2526. DOIhttps://doi.org/10.1007/s11053-024-10394-6.

13. Ali M.E., Cheema M.A., Hashem T., et al. Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions. 2024. PFG 92, 761–778. DOIhttps://doi.org/10.1007/s41064-024-00301-2.