Automatic Road Detection Using Object Oriented Deep Learning Algorithms and Global Training Data | ||
| Engineering Research Journal (Shoubra) | ||
| Volume 54, Issue 1, January 2025, Pages 317-325 PDF (1.4 M) | ||
| Document Type: Research articles | ||
| DOI: 10.21608/erjsh.2025.348642.1389 | ||
| Authors | ||
| Ahmed Nabil Elbahlol* ; Mahmoud Mohamed Hamed; Mahmoud Salah Gomaa | ||
| 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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