ABSTRACT Efficient extraction of roads from high‐resolution satellite images is critical for urban planning, disaster management and autonomous navigation, especially in complex urban environments. Existing segmentation techniques require significant manual effort and are prone to low accuracy, algorithms based on convolutional neural networks, such as U‐Net improve upon this. Still, their symmetrical encoder–decoder design fails to capture multi‐scale features, suffers from poor gradient flow and creates a semantic gap between encoded and decoded features. To mitigate these issues, we present MAGnet, a multiscale attention guided network that enhances road extraction by incorporating an attention guided regional feature block for multiscale feature fusion, employing squeeze and excitation for channel refinement, and addressing overfitting in conventional U‐shaped architectures. MAGnet integrates a focus gate system in skip connections to mitigate vanishing gradients and feature redundancy, alongside a tri‐level attention unit to bridge the disparity in information representation between the encoder and decoder through channel, spatial and pixel‐level attention. MAGnet achieves improved performance on benchmark datasets like Massachusetts Roads and DeepGlobe, with a more than 5% increase in dice coefficient and a 3% rise in mean intersection over union over top models. Its computational efficiency is underscored by a parameter count of 14.22M, 55.76 Giga floating‐point operations and 27.86 Giga multiply‐accumulate operations. Furthermore, MAGnet's decision‐making is enhanced by explainable artificial intelligence techniques for better interpretability. These results suggest that MAGnet offers a computationally efficient and interpretable approach to road extraction from high‐resolution satellite imagery.
Bashree et al. (Thu,) studied this question.
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