Automatic Road Detection Using Object Oriented Deep Learning Algorithms and Global Training Data | ||||
| Engineering Research Journal (Shoubra) | ||||
| Volume 54, Issue 1, January 2025, Page 317-325 PDF (1.4 MB) | ||||
| Document Type: Research articles | ||||
| DOI: 10.21608/erjsh.2025.348642.1389 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ahmed Nabil Elbahlol  ; Mahmoud Mohamed Hamed; Mahmoud Salah Gomaa
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| Geomatics Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt | ||||
| Abstract | ||||
| Automatic road extraction from satellite imagery is a critical task in remote sensing and urban planning, with applications in transportation network analysis, infrastructure development, and smart city solutions. This paper proposes a novel methodology for road detection by integrating object-oriented deep learning algorithms, specifically combining the Faster R-CNN architecture with the Multi-Task Road Extractor model to enhance road identification accuracy. The study utilizes SpaceNet satellite imagery data, focusing on urban areas, to train and evaluate the models. The Faster R-CNN model is employed to detect candidate road regions, while the Multi-Task Road Extractor model refines these detections by leveraging a shared encoder to perform simultaneous road segmentation and classification tasks. Experimental results demonstrate the effectiveness of this integrated approach, achieving an average precision (AP) of 0.557 at a 0.6 intersection-over-union (IoU) threshold with Faster R-CNN and a 98% accuracy after refinement with the Multi-Task model. These results highlight the potential of combining multi-task learning and object detection for improved road extraction in complex urban environments. | ||||
| Keywords | ||||
| Road Extraction; Faster R-CNN; Deep Learning; SpaceNet Dataset; Environmental Sustainability | ||||
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