Jalil, A., Alwan, I. (2025). A segmentation of buildings from multi-scene drone images using deep learning models: case study- a car multi-story garage in the camp of the university of technology. , 43(11), 947-955. doi: 10.30684/etj.2025.158830.1931
Akram H. Jalil; Imzahim A. Alwan. "A segmentation of buildings from multi-scene drone images using deep learning models: case study- a car multi-story garage in the camp of the university of technology". , 43, 11, 2025, 947-955. doi: 10.30684/etj.2025.158830.1931
Jalil, A., Alwan, I. (2025). 'A segmentation of buildings from multi-scene drone images using deep learning models: case study- a car multi-story garage in the camp of the university of technology', , 43(11), pp. 947-955. doi: 10.30684/etj.2025.158830.1931
Jalil, A., Alwan, I. A segmentation of buildings from multi-scene drone images using deep learning models: case study- a car multi-story garage in the camp of the university of technology. , 2025; 43(11): 947-955. doi: 10.30684/etj.2025.158830.1931

A segmentation of buildings from multi-scene drone images using deep learning models: case study- a car multi-story garage in the camp of the university of technology

Engineering and Technology Journal
Volume 43, Issue 11, November 2025, Pages 947-955    PDF (1.03 M)
Document Type: Research Paper
DOI: 10.30684/etj.2025.158830.1931
Authors
Akram H. Jalil* ; Imzahim A. Alwan
Civil Engineering Dept., University of Technology-Iraq, Alsinaa street, 10066 Baghdad, Iraq.
Abstract
Accurate building segmentation is essential for urban planning, monitoring, and mapping. Most deep learning approaches rely on single-view images, limiting segmentation accuracy due to the loss of spatial context. This re-search proposes a multi-view deep learning framework that integrates features extracted from four pre-trained CNN models—MobileNetV2, Res-Net50, VGG16, and InceptionV3—to capture multi-angle and multi-scale details. A vision transformer is employed to fuse and refine these features, enhancing global context and boundary precision. The proposed method was evaluated using UAV imagery of a multi-story garage at the University of Technology, captured by a DJI Mavic 2 Pro with a high-resolution Hasselblad L1D-20c camera. Experiments on the augmented building dataset achieved a segmentation accuracy of 93%, with notable improvements in Intersection over Union (IoU) and F1-score compared to standard CNN-based approaches. These results demonstrate the robustness of the model under varying lighting and occlusion conditions and highlight its potential for high-precision urban building segmentation.

Highlights
  • A novel multi-view deep learning framework was developed using MobileNetV2, ResNet50, VGG16, and InceptionV3.
  • The framework achieved a segmentation accuracy of 93%, surpassing conventional building segmentation methods.
  • Features were integrated with a Vision Transformer to enhance segmentation performance.
  • The approach was validated on UAV imagery of a multi-story garage at the University of Technology.
  • Strong potential for urban planning, drone mapping, and image analysis applications.
Keywords
Segmentation Deep learning Pre; trained models Vision transformer Multi; views Drone images
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