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Advanced Semi-Automatic Approach for Identifying Damaged Surfaces in Cultural Heritage Sites: Integrating UAVs, Photogrammetry, and 3D Data Analysis

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dc.contributor.author Caciora, Tudor
dc.contributor.author Ilies, Alexandru
dc.contributor.author Herman, Grigore Vasile
dc.contributor.author Berdenov, Zharas
dc.contributor.author Safarov, Bahodirhon
dc.contributor.author Bilalov, Bahadur
dc.contributor.author Ilies, Dorina Camelia
dc.contributor.author Baias, Stefan
dc.contributor.author Hassan, Thowayeb H.
dc.date.accessioned 2026-03-02T06:34:09Z
dc.date.available 2026-03-02T06:34:09Z
dc.date.issued 2024
dc.identifier.citation Caciora, T.; Ilies, , A.; Herman, G.V.; Berdenov, Z.; Safarov, B.; Bilalov, B.; Ilies, , D.C.; Baias, S, .; Hassan, T.H. Advanced SemiAutomatic Approach for Identifying Damaged Surfaces in Cultural Heritage Sites: Integrating UAVs, Photogrammetry, and 3D Data Analysis. Remote Sens. 2024, 16, 3061. https://doi.org/10.3390/rs16163061 ru
dc.identifier.issn 2072-4292
dc.identifier.other doi.org/10.3390/rs16163061
dc.identifier.uri http://repository.enu.kz/handle/enu/29598
dc.description.abstract The analysis and preservation of the cultural heritage sites are critical for maintaining their historical and architectural integrity, as they can be damaged by various factors, including climatic, geological, geomorphological, and human actions. Based on this, the present study proposes a semiautomatic and non-learning-based method for detecting degraded surfaces within cultural heritage sites by integrating UAV, photogrammetry, and 3D data analysis. A 20th-century fortification from Romania was chosen as the case study due to its physical characteristics and state of degradation, making it ideal for testing the methodology. Images were collected using UAV and terrestrial sensors and processed to create a detailed 3D point cloud of the site. The developed pipeline effectively identified degraded areas, including cracks and material loss, with high accuracy. The classification and segmentation algorithms, including K-means clustering, geometrical features, RANSAC, and FACETS, improved the detection of destructured areas. The combined use of these algorithms facilitated a detailed assessment of the structural condition. This integrated approach demonstrated that the algorithms have the potential to support each other in minimizing individual limitations and accurately identifying degraded surfaces. Even though some limitations were observed, such as the potential for the overestimation of false negatives and positives areas, the damaged surfaces were extracted with high precision. The methodology proved to be a practical and economical solution for cultural heritage monitoring and conservation, offering high accuracy and flexibility. One of the greatest advantages of the method is its ease of implementation, its execution speed, and the potential of using entirely open-source software. This approach can be easily adapted to various heritage sites, significantly contributing to their protection and valorization. ru
dc.language.iso en ru
dc.publisher Remote Sens ru
dc.relation.ispartofseries 16, 3061;
dc.subject cultural heritage ru
dc.subject photogrammetry ru
dc.subject 3D data analysis ru
dc.subject degradation detection ru
dc.subject point cloud processing ru
dc.subject algorithms ru
dc.subject structural analysis ru
dc.title Advanced Semi-Automatic Approach for Identifying Damaged Surfaces in Cultural Heritage Sites: Integrating UAVs, Photogrammetry, and 3D Data Analysis ru
dc.type Article ru


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