Article: Development of an AI-powered classification model for monitoring construction disturbances using remote sensing data Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.37 pp.41 - 58 Abstract: In complex settings, the identification and dynamic monitoring of construction disturbance areas still face problems such as insufficient feature extraction, limited generalisation, and unstable multi-temporal detection accuracy. This study proposed a novel multi-level integrated approach that combines fractal network evolution algorithm (FNEA) segmentation, genetic algorithm (GA) global optimisation, and convolutional neural network (CNN) multi-scale feature learning to achieve high-precision disturbance recognition and dynamic monitoring. Experimental results showed that the method achieved an overall accuracy of 95.2% ± 0.4% (95% CI [95.0, 95.4]), maintained an accuracy above 99% in multi-temporal tests, reduced the false alarms and missed detections by 0.7-5.2% compared with baseline methods, and converged within 30 iterations. Compared with existing techniques, the framework provides an intelligent and efficient solution through the joint use of image segmentation, evolutionary optimisation, and deep feature learning, opens a new direction for remote sensing monitoring in complex construction environments. Inderscience Publishers - linking academia, business and industry through research

Title: Development of an AI-powered classification model for monitoring construction disturbances using remote sensing data

Authors: Jing Yan; Yin Liu; Shengcheng Xie; Yimin An; Yifan Liang

Addresses: Marketing Service Center, State Grid Xinjiang Electric Power Co., Ltd., Urumqi, 830000, Xinjiang, China ' Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi, 830000, Xinjiang, China ' Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi, 830000, Xinjiang, China ' Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi, 830000, Xinjiang, China ' Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd., Urumqi, 830000, Xinjiang, China

Abstract: In complex settings, the identification and dynamic monitoring of construction disturbance areas still face problems such as insufficient feature extraction, limited generalisation, and unstable multi-temporal detection accuracy. This study proposed a novel multi-level integrated approach that combines fractal network evolution algorithm (FNEA) segmentation, genetic algorithm (GA) global optimisation, and convolutional neural network (CNN) multi-scale feature learning to achieve high-precision disturbance recognition and dynamic monitoring. Experimental results showed that the method achieved an overall accuracy of 95.2% ± 0.4% (95% CI [95.0, 95.4]), maintained an accuracy above 99% in multi-temporal tests, reduced the false alarms and missed detections by 0.7-5.2% compared with baseline methods, and converged within 30 iterations. Compared with existing techniques, the framework provides an intelligent and efficient solution through the joint use of image segmentation, evolutionary optimisation, and deep feature learning, opens a new direction for remote sensing monitoring in complex construction environments.

Keywords: construction disturbance detection; fractal network evolution; genetic algorithm; convolutional neural network; CNN; multi-feature fusion.

DOI: 10.1504/IJICT.2025.149177

International Journal of Information and Communication Technology, 2025 Vol.26 No.37, pp.41 - 58

Received: 17 Jun 2025
Accepted: 01 Sep 2025
Published online: 16 Oct 2025 *

Title: Development of an AI-powered classification model for monitoring construction disturbances using remote sensing data

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