Deep learning for building segmentation and change detection in urban monitoring: ‘Study case: Zenata City’

Abstract Urban monitoring has become a field of interest in small cities to track the growth of urban areas in different regions around the world. Semantic segmentation of buildings through drone imagery has emerged as an effective solution. In the present research, the efficiency of three deep learning models, U-Net, SegNet, and a proposed hybrid algorithm, was evaluated. Several metrics, including precision, recall, mean intersection over union (mIoU), intersection over union (IoU), F1 score, accuracy, and loss function, were used to assess the quality of segmentation. A test set of 133 annotated image tiles was used for evaluation. The results showed that the proposed algorithm was resistant to varying urban textures and achieved an accuracy of 98% with a loss function value of 6%. In comparison, U-Net achieved 97% accuracy with a loss function value of 9%, and SegNet achieved 95% accuracy with a loss function value of 11%. The study’s results are constrained by the fact that it used the Zenata City urban data, which might limit its utility to other urban areas. This comparative analysis advanced automated urban monitoring by offering interesting details about the strengths and limitations of each model, thus guiding the choice of the most suitable image segmentation technique for building and construction applications. This study demonstrates practical applications in urban planning and construction analysis by automating building detection using deep learning. Accurate building segmentation from drone imagery improves land use planning, infrastructure development, and smart city decision-making, ultimately contributing to more effective urban management.