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RoadTransNet: advancing remote sensing road extraction through multi-scale features and contextual information

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Abstract

Road extraction is a crucial task that requires high-resolution remote sensing images with wide-ranging applications in urban planning, navigation, and autonomous vehicles. However, this task is challenged by complex road structures and the need to capture long-range dependencies. RoadTransNet is a new road extraction architecture that aims to solve these problems that making the power of the Swin Transformer and Feature Pyramid Network (FPN) while introducing Transformer-like attention mechanisms. RoadTransNet combines a robust convolutional backbone, inspired by the Swin Transformer, with an FPN to capture multi-scale features effectively. The Transformer-like attention mechanisms, including multi-head self-attention and cross-attention, enable the network to represent context information on a local and global scale, ensuring accurate road extraction. The skip connections facilitate gradient flow, preserving fine details, and decoding layers transform extracted features into precise road predictions. Our experiments are conducted using the RoadTransNet, which is subject to rigorous assessment on the following datasets: the DeepGlobe road extraction challenge Dataset and the CHN6-cUG roads dataset. The outcomes indicate its superior performance in achieving high-level metrics of precision and recall, as well as achieving high F1 scores and IoU. The comparative evaluations performed against traditional methods showcase RoadTransNet's ability to capture complex road structures and long-range dependencies. The RoadTransNet stands as a comprehensive solution for the extraction of roads in high-resolution remote sensing images, offering promising opportunities for improving urban planning, navigation systems, and autonomous vehicle technologies. Its success lies in the synergy of convolutional and transformer-based architectures, paving the way for advanced remote sensing applications in smart cities and others.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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  1. Department of Electronics and Communication Engineering, PET Engineering College, Valliyur, Tamilnadu, India

    K. Madhan Kumar

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  1. K. Madhan Kumar
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KMK agreed on the content of the study. KMK collected all the data for analysis. KMK agreed on the methodology. KMK completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Correspondence to K. Madhan Kumar.

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Kumar, K.M. RoadTransNet: advancing remote sensing road extraction through multi-scale features and contextual information. SIViP 18, 2403–2412 (2024). https://doi.org/10.1007/s11760-023-02916-1

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