Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas

Evgenie Lebedev; Sergey Gruzdev

doi:10.30533/scidata-2023-14-07

VAK Russia 1.6.20 UDC 004.932.2 CSCSTI 28.23 UDC 528.88 CSCSTI 47.49

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IMPROVING TECHNOLOGIES FOR ENHANCING THE QUALITY OF OPEN DATA IN DIGITAL TERRAIN MODELS FOR URBANIZED AREAS Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas

АА Аа

Published in Spatial Data: Science, Research and Technology · Volume 14, Issue 4 · Pages 47–61 · Rubric: Geoinformatics, cartography

DOI: https://doi.org/10.30533/scidata-2023-14-07

Received: 07.08.2023 Accepted: 15.12.2023 Published: 17.10.2025

Authors

Evgenie Lebedev

Sergey Gruzdev

Evgenie Lebedev

Sergey Gruzdev

ABSTRACT

This study is dedicated to addressing the challenges associated with enhancing the processing and analysis of open data related to digital terrain models, with a specific focus on urbanized areas. The research investigates contemporary technologies aimed at advancing the accuracy and quality of presented geospatial data. The study conducts a comprehensive review and analysis of existing global digital terrain models. Through the application of the proposed evaluation method, the ALOS dataset is selected as the primary data source. Based on this dataset, a meticulously constructed training set is developed to facilitate the experimentation of an innovative neural network approach. The research delves into the implementation of a hybrid neural network architecture, strategically composed of distinct modules for extracting coordinate information and subsequent processing through a deep model. This approach synergistically leverages the strengths of each module to more effectively account for the intricate features of urbanized landscapes, thereby yielding substantially enhanced precision in analysis outcomes. The practical implications of this research are profound and extend across various domains, notably urban planning and territorial management.

KEYWORDS

convolutional neural network (CNN), DEM, radar survey, SAGA GIS, data cleaning

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GOST

Lebedev E. D., Gruzdev S. S. Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas // Spatial Data: Science, Research and Technology. 2025. no. 4. pp. 47-61. DOI: https://doi.org/10.30533/scidata-2023-14-07. (Date of access 21.09.2026).

MLA

Evgenie Lebedev, Gruzdev S. S.. "Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas." Spatial Data: Science, Research and Technology 14.4 (2025): 47-61. DOI: https://doi.org/10.30533/scidata-2023-14-07.

APA

Lebedev E. D., Gruzdev S. S. (2025). Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas. Spatial Data: Science, Research and Technology, 14(4), 47-61. https://doi.org/10.30533/scidata-2023-14-07

Chicago

Evgenie Lebedev, Gruzdev S. S.. "Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas." Spatial Data: Science, Research and Technology 14, no. 4 (2025): 47-61. https://doi.org/10.30533/scidata-2023-14-07.

WIKI

{{cite journal | author = Lebedev E. D., Gruzdev S. S. | title = Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas | journal = Spatial Data: Science, Research and Technology | year = 2025 | volume = 14 | issue = 4 | pages = 47-61 | doi = 10.30533/scidata-2023-14-07 }}

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IMPROVING TECHNOLOGIES FOR ENHANCING THE QUALITY OF OPEN DATA IN DIGITAL TERRAIN MODELS FOR URBANIZED AREAS Improving technologies for enhancing the quality of open data in digital terrain models for urbanized areas

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