THE SIGNIFICANCE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN CARTOGRAPHIC AND CADASTRAL ACTIVITIES AND PROSPECTS FOR THEIR ADVANCEMENT
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artificial intelligence, cartography, cadastre, GeoAI, machine learning, deep learning, geospatial data, land use mapping, UAV, remote sensing, convolutional neural network, boundary detection, land administration.Abstrak
This article analyzes the implementation of artificial intelligence (AI) technologies in cartography and cadastral activities, examining their practical significance and future prospects. The study investigates the application of machine learning, deep learning, computer vision, and generative AI technologies in geospatial domains, drawing on international scientific literature and Uzbekistan's national experience. Research findings demonstrate that AI technologies are creating significant opportunities in automated land boundary detection, digitization of cadastral data, land use mapping, and modernization of land management systems. At the same time, challenges related to data quality, accuracy, legal regulation, and a shortage of qualified personnel were identified. The article analyzes state policy in Uzbekistan and international experience, outlining future development prospects.
Iqtiboslar
Zhang, X., Shi, Q., Sun, Y. et al. (2024). The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data. Journal of Geodesy and Geoinformation Science, 7(3), 1–23. https://doi.org/10.11947/j.JGGS.2024.0301
Li, W. et al. (2025). Artificial Intelligence in Earth Science: A GeoAI Perspective. Journal of Geophysical Research: Machine Learning and Computation. https://doi.org/10.1029/2025JH000691
Hosseini, H., Atazadeh, B., & Rajabifard, A. (2025). Artificial Intelligence for Querying Land and Property Data from Cadastral Plans. FIG Working Week 2025, Paper TS07I_13013.
Marzougui, M. et al. (2025). Deep Learning-Based Spatial Pattern Modeling for Land Use and Land Cover Classification Using Satellite Imagery. Meteorological Applications. https://doi.org/10.1002/met.70064
Bruns, J. et al. (2025). Towards Intelligent Land Administration Systems: Research Challenges, Applications and Prospects in AI-Driven Approaches. ScienceDirect. https://doi.org/10.1016/j.landusepol.2025
Wang, C. et al. (2024). Envisioning Generative Artificial Intelligence in Cartography: Mapmaking, Map Use, and Ethics. International Journal of Cartography. https://doi.org/10.1080/23729333.2025.2582231
Petitpierre, R. et al. (2024). Posthuman Cartography? Rethinking Artificial Intelligence, Cartographic Practices, and Reflexivity. Annals of the American Association of Geographers, 115(3), 499–512.
Cetl, V. et al. (2022). Revising Cadastral Data on Land Boundaries Using Deep Learning in Image-Based Mapping. ISPRS International Journal of Geo-Information, 11(5), 298. https://doi.org/10.3390/ijgi11050298
Dhakal, K., & Sastry, S. (2024). Sat2Cap: Text-guided Global Satellite Imagery Generation. EarthVision Workshop, CVPR 2024.
Liu, J. et al. (2026). Using GeoAI and Machine Learning Tools for Consistent High-Resolution Land Cover Mapping Based on Time-Series NAIP Imagery. https://doi.org/10.21203/rs.3.rs-8340981/v1
Matchanov, O.J., Matchanov, M.J. (2024). Web Cartography [Textbook]. Tashkent: Bookmany Print, 186 p.
Decree of the President of the Republic of Uzbekistan No. PQ-358, dated October 14, 2024, "On Approving the Strategy for the Development of Artificial Intelligence Technologies until 2030." https://www.lex.uz/docs/-7158604
Resolution No. 425 of the Cabinet of Ministers of the Republic of Uzbekistan, dated July 10, 2025. https://gov.uz
Cadastre Agency (2024). 2024 Annual Report on Geodesy Oversight. https://kadastr.uz
Lemenkova, P. (2025). Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy. Journal of Imaging, 11(5), 153. https://doi.org/10.3390/jimaging11050153
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