MODERN APPROACH TO SOLVING PROBLEMS OF SPATIAL INFORMATION COLLECTION AND ANALYSIS
DOI:
https://doi.org/10.33042/2522-1809-2025-3-191-401-407Keywords:
geodesy, technologies, GIS, programming, spatial data, geodetic equipment, artificial neural networkAbstract
The paper outlines the characteristics of modern geodetic instruments and highlights that current surveying methodologies require improved approaches to solving applied tasks, given the continuous advancement of geodetic technologies.
An analysis of current geodetic software demonstrates that modern applications are designed to harness the capabilities of spatial data acquisition instruments and automate data collection processes. However, their functionality may be limited when it comes to highly specialized or non-standard geodetic tasks.
The study identifies promising directions for integrating programming techniques into spatial analysis. The effective use of point clouds, artificial neural networks (ANNs) in ArcGIS Pro, and Python-based modules expands the range of available tools for geodetic research and problem-solving.
Today, geodetic equipment has reached a new stage of development, not only through mechanical improvements of measurement instruments but also via advancements in embedded software and specialized applications for interpreting and processing data collected in the field. This technological evolution drives the development of new methods for optimizing surveying workflows and geospatial data analysis. Among these, the creation of custom software solutions emerges as one of the most efficient and practical approaches.
Furthermore, modern software platforms now offer significantly broader capabilities for addressing day-to-day geodetic tasks, enabling the collection of larger volumes of accurate spatial data. This, in turn, opens up more extensive opportunities for data analysis. Nonetheless, certain complex or non-standard geodetic problems may still require manual intervention or the use of tailor-made scripts created by geodetic professionals, using tools such as neural networks and custom code.
This paper conceptually explores the feasibility of solving such tasks using additional programming functionality. ArcGIS, for instance, offers dedicated geospatial processing modules, including a built-in Python interpreter. This interpreter provides access to the ArcPy library, which enables geospatial data conversion, management, and the creation of robust analytical workflows for geodetic applications.
By leveraging the Python programming language, the ArcPy package, and a suite of spatial analysis tools, users can implement advanced operations on spatial data—thus significantly enhancing the capabilities of GIS specialists and geodetic engineers.
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