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While high-speed railway provides safe daily transportation for millions, its operational security is increasingly threatened by external hazards. High-speed railway hazards detection from remote sensing remains challenging due to scale variations and target-background ambiguity. To address these challenges, we propose the Multiscale Context-Aware Network (MSCANet) to capture spatial details and model multiscale contextual information. To extract spatial details, we develop the Local Feature Enhancement Module (LFEM) to aggregate parallel convolutions with diverse receptive fields, thereby enhancing the localization of multi-scale hazards. To capture long-range dependencies while ensuring computational efficiency, the Efficient Transformer Block (ETB) models local pixel similarity relationships via window-based attention, enabling precise discrimination of hazards from complex backgrounds. For feature integration, the Multiscale Feature Refinement Module (MFRM) iteratively integrates hierarchical features to aggregate contextual information. MSCANet achieves the highest mIoU of 87.73%, outperforming leading models on the hazards dataset. MSCANet is further applied to a high-traffic section of the Beijing-Shanghai High-Speed Railway between Jinan West Station and Qufu East Station, generating a detailed hazards map with distinct boundaries. The encouraging results indicate that MSCANet holds promise for high-speed railway hazards detection and presents substantial potential in hazards management. The code is available at https://github.com/smallbingbing/mscanet.

High-speed railway hazards detection from GF-2 imagery with multiscale context-aware network