MODERN ASPECTS OF METHODOLOGICAL SUPPORT OF GEODETIC MONITORING OF OBJECTS AND TERRITORIES

Abstract

This study is devoted to the analysis of modern methods and technologies of geodetic monitoring used to detect and control changes in geodynamic processes and structural deformations of infrastructure and natural systems. Geodetic monitoring is an integral part of infrastructure safety, environmental protection, and effective land management, as it allows assessing the stability of buildings, bridges, dams, mountain ranges, and other important structures, as well as natural landscapes. This study highlights the development and integration of innovative monitoring methods, such as GNSS, laser scanning, unmanned aerial vehicles, and other geodetic technologies.
The analysis of previous studies shows that the use of GNSS and laser scanning provides high accuracy and regularity of measurements, which is critical for real-time monitoring of infrastructure and for predicting possible deformations and displacements. The use of robotic total stations and automated networks reduces dependence on the human factor and speeds up data collection, ensuring a high level of measurement accuracy. At the same time, data processing using machine learning and artificial intelligence methods opens up opportunities for predicting possible changes in the stability of objects, which significantly increases the efficiency of geodetic monitoring.
The purpose of the study is to substantiate the need to use the latest technologies in geodetic monitoring to achieve high accuracy and timely detection of changes that may have critical safety implications. In particular, the use of GNSS in combination with automated total stations allows for continuous monitoring of large areas and detection of even minor changes in the structure of objects. The article discusses in detail the methods of camera processing of data obtained during laser scanning, which allows creating high-precision three-dimensional models for further analysis of changes.
It has been determined that the combination of GNSS, laser scanning and data analysis using machine learning technologies can achieve the reliability and accuracy required to monitor critical infrastructure facilities. Technological advances allow not only to detect current changes, but also to predict possible trends, ensuring timely response to risks and threats. The results of this study can be applied in the field of territory management, environmental protection, and the design of sustainable engineering solutions.