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A Python-Based Framework for the Transformation and Visualization of GNSS Data in Engineering and Geospatial Applications.

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Název: A Python-Based Framework for the Transformation and Visualization of GNSS Data in Engineering and Geospatial Applications.
Autoři: Bălă, Alina Corina, Brebu, Floarea-Maria
Zdroj: Journal of Applied Engineering Sciences; Dec2025, Vol. 15 Issue 2, p207-212, 6p
Témata: PYTHON programming language, SOFTWARE frameworks, GEOGRAPHIC information systems, OPEN source software, VISUALIZATION, GLOBAL Positioning System, DATA transformations (Statistics)
Abstrakt: Global Navigation Satellite System (GNSS) observables are standardized in RINEX files, but most existing tools for position extraction are fragmented and require manual intervention. This paper introduces a Python-based framework that automates the processing of RINEX 2.11 observation files by extracting approximate receiver positions, transforming them into the WGS84 system, filtering invalid data, and exporting results in structured formats. The workflow produces multi-format outputs (CSV, KML, interactive maps), enabling direct integration with engineering and geospatial applications. Implemented in Google Colab with open-source libraries, the framework was validated on real datasets, demonstrating reproducibility, modularity, and scalability. The proposed approach supports diverse applications, including surveying, cadastral mapping, geodetic monitoring, and spatial data science. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Global Navigation Satellite System (GNSS) observables are standardized in RINEX files, but most existing tools for position extraction are fragmented and require manual intervention. This paper introduces a Python-based framework that automates the processing of RINEX 2.11 observation files by extracting approximate receiver positions, transforming them into the WGS84 system, filtering invalid data, and exporting results in structured formats. The workflow produces multi-format outputs (CSV, KML, interactive maps), enabling direct integration with engineering and geospatial applications. Implemented in Google Colab with open-source libraries, the framework was validated on real datasets, demonstrating reproducibility, modularity, and scalability. The proposed approach supports diverse applications, including surveying, cadastral mapping, geodetic monitoring, and spatial data science. [ABSTRACT FROM AUTHOR]
ISSN:22473769
DOI:10.2478/jaes-2025-0024