Directions of use of automation tools in Ukrainian farms

  1. Home
  2. Archive
  3. № 2 (71)
Author(s) Collection number Pages Download abstract Download full text
Златов А. Ф., Трішин Ф. А. № 2 (71) 64-72 Image Image

The formation of an innovative architectural model of digital cartography integrating artificial intelligence is based on a multi-level approach that includes the collection and processing of geospatial data, their in-depth analysis through neural networks, and cognitive interpretation with visualisation in augmented reality. The first level involves the aggregation of information from various sources, its normalisation, and segmentation using machine learning algorithms. The second level focuses on deep data analysis employing convolutional neural networks (CNNs), which enable the automatic detection and classification of objects. The third level is dedicated to the cognitive processing of results and the creation of intuitive graphical layers, where augmented reality technologies are applied to enhance user convenience and the functionality of cartographic products.

Conceptually, the innovative model is grounded in the interaction between the levels, as illustrated in the schematic representation, which enables the system to achieve high performance across a wide range of tasks – from environmental monitoring and urban management to emergency response. This approach is characterised by mobility, scalability, and self-learning capability, creating the foundation for the development of advanced intelligent cartographic platforms capable of promptly responding to changes and supporting informed decision-making at various levels of governance. Furthermore, the integration of intelligent technologies into digital cartography enhances the quality of spatial data through automated processes of error detection and correction, thereby improving the reliability and accuracy of cartographic products. This establishes favourable conditions for their extensive application across diverse scientific and practical domains, including ecology, urban studies, transport, and security.

Keywords: Digital cartography, AI, CNN, Cognitive interpretation, Augmented reality.

doi: 10.32403/1998-6912-2025-2-71-54-63


  • 1. Wang, J. (2022). Cartography: From digital to intelligent. Geomatics and information science of Wuhan university, 47(12), 1963-1977. https://doi.org/10.13203/j.whugis20220780.
  • 2. Kang, Y., Gao, S., & Roth, R. E. (2024). Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics. Cartography and Geographic Information Science, 51(4), 599-630. https://doi.org/10.1080/15230406.2023.2295943.
  • 3. Yonov, N. (2024). Map of Tomorrow: How AI is Transforming Modern Cartography. In 9th International Conference on Cartography and GIS, 20, 52.
  • 4. Cramer, N. C. (2025). From paper maps to artificial intelligence: a historical analysis of cartography and geospatial science.
  • 5. Dudun, T. & Bondarenko E. (2025). Zastosuvannya system shtuchnoho intelektu u pidhotov­tsi fakhivtsiv z kartohrafiyi ta heoinformatyky v Ukrayini, Informatsiyni tekhnolohiyi i zasoby navchannya, 106(2), 192-210. https://doi.org/10.33407/itlt.v106i2.6008 (in Ukrainian).
  • 6. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031.
  • 7. ESRI. (2024). Unlocking GeoAI: Smarter spatial analysis with ArcGIS. ESRI Resource Hub. https://resource.esriuk.com/blog/geoai-smarter-spatial-analysis-with-arcgis/.
  • 8. TensorFlow. (2024). TensorFlow. TensorFlow API Documentation. https://www.tensorflow.org.
  • 9. Machine Learning for QGIS - Machine Learning for Image Classification in QGIS. (n. d.). QGIS Plugins. https://plugins.qgis.org/plugins/ml4qgis/.
  • 10. He, D., & Si, X. (2021). Image processing design and algorithm research based on cloud computing. Journal of Sensors, 2021 1-10. https://doi.org/10.1155/2021/9198884.
  • 11. Baig, M. M. J., Wang, Y. L., Chung, S. H., & Stepanyants, A. (2024). Normalized level set model for segmentation of low-contrast objects in 2- and 3- dimensional images. bioRxiv : the preprint server for biology, 2024.01.12.574651. https://doi.org/10.1101/2024.01.12.574651.
  • 12. Brownlee, J. (2022). Crash course in convolutional neural networks for deep learning. Machine Learning Mastery. https://machinelearningmastery.com/crash-course-convolutio­nal-neu­ral-networks/.
  • 13. Jamil, F., Kashif, A., Alharbi, O., Zafar, S., Aljaedi, A., & Qasaymeh, Y. (2024). Cognitive Ana­lysis of Neural Networks using Fractional Metric Dimension and Applications. IEEE Access, 1. https://doi.org/10.1109/access.2024.3370472.
  • 14. ESRI. (2023). How to cite ArcGIS maps and data. Resource Hub. https://resources.esri.ca/education-and-research/how-to-cite-arcgis-maps-and-data.
  • 15. Tan, K. S., Lam, C. K., Tan, W. C., Ooi, H. S., & Lim, Z. H. (2024). A review of image pro­cessing and quantification analysis for solid oxide fuel cell. Energy and AI, 16, 100354. https://doi.org/10.1016/j.egyai.2024.100354.