Authors:
Lei Xin,Zhao Chen,Zhenish T. Beksultanov,Fu Zh. LiGulmira Isaeva,DOI NO:
https://doi.org/10.26782/jmcms.2026.06.00001Keywords:
convolutional neural network,semantic segmentation,construction,digital transformation,smart city,urbanization.,Abstract
The predominantly poor condition of the existing multi-apartment housing stock and a sharp increase in the number of new buildings in the Kyrgyz Republic require the introduction of modern methods of automated monitoring and control. The purpose of this research is to develop a method for the rapid detection and classification of defects on the facades of civil buildings and structures. The methodology of the research comprises methods of structural analysis, manual annotation of graphical data, deep learning, and statistical analysis. These methods enabled the definition of facade defects and their classification relative to the residential building model, the development of an original dataset with an array of images with characteristic defects, and the selection and training of a convolutional neural network model. The article presents the characteristics of the model and the statistical data of its operation when processing test data. The results demonstrate high accuracy and performance, which makes it possible to use the proposed model to create a digital ecosystem for urban management. Such data can be used for automated management, planning services for repair, and improving the efficiency of buildings and structures.Refference:
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