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Vol. 3 No. 11 (2026)
Engineering & Education

Articles

TARIXIY-MADANIY MEROS HUDUDLARIDA GEODEZIK MONITORING VA KARTOGRAFIK MODELLASHTIRISHDA SUN'IY INTELLEKT HAMDA NEYRON TARMOQLARNI QO'LLASHNING FUNKSIONAL-TEXNIK IMKONIYATLARI

DOI 10.63294/isja/vol3iss11/302 Google Scholar Google Scholar Pages 47-56

Published 2026-08-31

Authors
Yo‘ldoshev Jahongir Odilbek o‘g‘li a , Safarov E.Y. b , Prenov Sh.M. c
Affiliations
a Alfraganus universiteti Turizm fakulteti Umumkasbiy fanlar kafedrasi o‘qituvchisi
b O‘zMU Geodeziya, kartografiya va kadastr kafedrasi t.f.d. professori
c Aniq va ijtimoiy fanlar universiteti O‘quv-uslubiy bo‘lim boshlig‘i
Alfraganus — Vol. 3, Issue 11 (2026)
PDF (Uzbek)

Abstract

This article analyzes the functional and technical capabilities of artificial intelligence technologies and neural network algorithms—specifically within geodetic monitoring and cartographic modeling processes—for ensuring the geo-ecological stability of the cities of Tashkent, Samarkand, and Khiva, which house significant historical and cultural heritage sites. The study aims to develop scientific and methodological foundations for integrating data from remote sensing, radar interferometry (InSAR), and geographic information systems (GIS) with deep learning algorithms. The research methodology employed techniques such as land cover and building density segmentation using convolutional neural networks (CNN), climate and temperature dynamics forecasting based on time series via long short-term memory (LSTM) networks, and the classification of subsidence and degradation risks using Random Forest and gradient boosting (LightGBM/GBDT) algorithms. As a result, a generalized functional scheme and a comparative table of algorithms were developed to monitor the geotechnical condition of historical monuments in all three cities; specific primary geo-ecological risk factors were identified: moisture and subsidence for the inner city of Khiva; soil salinity and dome deformation for Samarkand’s architectural complexes; and rapid urbanization pressure for Tashkent’s historical quarters. The research findings serve to develop practical recommendations for integrating the AI-based monitoring module into the National Geoportal and the Unified Cadastre System, and form the methodological basis for the subsequent chapters of the dissertation.

Keywords

artificial intelligence, neural networks, geodetic monitoring, cartographic modeling, geo-ecological stability, historical-cultural heritage, InSAR, remote sensing, deep learning, National Geoportal

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