Аннотации:
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.
