Automatic Road Detection Using Object Oriented Deep Learning Algorithms and Global Training Data

Elbahlol, Ahmed Nabil; Hamed, Mahmoud Mohamed; Gomaa, Mahmoud Salah

doi:10.21608/erjsh.2025.348642.1389

Automatic Road Detection Using Object Oriented Deep Learning Algorithms and Global Training Data

Document Type : Research articles

Authors

Geomatics Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt

10.21608/erjsh.2025.348642.1389

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.

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