Hybrid Quantum CNN-Based Information Technology for Building Semantic Segmentation in Aerial Imagery

ENG: This paper presents information technology for semantic segmentation of building objects in high-resolution aerospace images using a hybrid quantum convolutional neural network (QCNN). Quantitative and visual evaluations demonstrate the effectiveness of the proposed approach compared to classical segmentation models such as U-Net, CNN, and FCN. The hybrid QCNN model achieved high pixel classification accuracy, with a mean accuracy of 0.98 on the training set and 0.97 on the validation set, indicating robust object recognition. Loss values reached minimal levels (0.05 training, 0.07 validation), confirming efficient training without overfitting. Intersection over Union (IoU) scores were 0.90 and 0.89 for training and validation sets, respectively, demonstrating precise building contour delineation. Testing on diverse urban scenes yielded 100% detection accuracy without false positives or negatives. Training dynamics showed rapid convergence, with mean average precision (mAP) increasing to 0.35–0.45 and mAP@50:95 reaching 0.20–0.30, stabilizing after 50 epochs. The proposed technology effectively segments buildings under varying density, geometric complexity, shadows, and background noise. The hybrid QCNN approach enhances segmentation quality and generalization to new images. The results confirm the practical applicability of the developed technology for geospatial monitoring, urban planning, and mapping based on aerospace imagery.