A frequency-domain and context-guided deep learning transformer architecture for real

Abstract Landslides are highly destructive geological disasters that pose severe threats to human life, property, and ecological environments. Traditional detection methods struggle with insufficient accuracy, poor real-time performance, and limited adaptability to complex terrains. While deep learning technologies have significantly advanced automated landslide detection, current models still underperform when dealing with multi-scale targets, small-scale landslides, and resource-constrained environments. To address these challenges, this paper proposes an enhanced landslide detection method based on the RT-DETR-r18 framework, aiming to improve detection accuracy, efficiency, and robustness. Specifically, our contributions are threefold: (1) We design a lightweight fusion module, FFCMC2f, which integrates spatial- and frequency-domain features to boost the model’s global perception and local detail expression. (2) We introduce an adaptive deformable attention module, AIFI-DAttention, which combines reference point offsets and multi-head attention mechanisms to strengthen the modeling of complex structures and scale-variant targets. (3) We integrate a context-guided module, ContextGuidedBlock, which captures local details, contextual semantics, and global structures simultaneously to enhance robustness against complex backgrounds and small targets. Experimental results demonstrate that the proposed model achieves consistent improvements across key metrics. Compared to the baseline, precision increased by 3.7% (from 74.3 to 78.0%), recall rose from 67.4 to 69.4%, and mAP@0.5 improved from 66.2 to 69.3%. Furthermore, the model maintains a high inference speed of 161.2 FPS with only 21.6 M parameters and 60.1 GFLOPs. 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Author information Authors and Affiliations Contributions ZG contributed to the conceptualization, formal analysis, original draft writing, method, project administration, and manuscript review and editing and provided software. Corresponding author Ethics declarations Conflict of interest The authors declare no competing interests. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Geng, Z. A frequency-domain and context-guided deep learning transformer architecture for real-time landslide detection. J Supercomput 82, 714 (2026). https://doi.org/10.1007/s11227-026-08631-1 Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1007/s11227-026-08631-1