GLSNet: State-Space-Enhanced Open-Pit Mine Detection With Global-Local Information Fusion

Abstract：Open-pit mines, typical land-surface features shaped by intensive human activities, require rapid identification of their spatial distribution for effective mineral resource supervision, ecological disturbance assessment, and land inspection. Optical remote sensing imagery, with its wide coverage, convenient acquisition, and rich spatial details and textures, provides intuitive morphological and contextual cues for open-pit mine identification and is therefore widely employed in routine monitoring and rapid assessment. Nevertheless, open-pit mines often bear strong visual similarities to quarries, bare land, construction-disturbed zones, and waste dumps. Meanwhile, slender structures (e.g., pit boundaries, bench slopes, and haul roads) tend to be smoothed out in multi-scale representations, which makes it challenging to balance global shape characterization with precise local boundary localization. To address these issues, we propose GLSNet (Global-Local State-space Network) , a feature-enhancement framework for open-pit mine detection, consisting of three synergistic modules. First, an Adaptive Scale-aware Spatial Pyramid Pooling Fast (A-SPPF) module is introduced to adaptively select effective contextual ranges, suppress confusing background interference, and improve scale robustness. Second, a Low-resolution State-Space Modeling (LS-SSM) module is designed to efficiently model long-range dependencies and scene structural relationships, enhancing discrimination between open-pit mines and visually similar land-surface units. Third, a Scale-adaptive Global–Local Fusion (SGF) module is proposed to jointly strengthen global structural constraints and local boundary details, thereby balancing holistic morphology representation and key boundary localization, and improving detection stability and cross-region generalization.We evaluate our method on the public Open Pit Mine Object Detection Dataset and compare it with Faster R-CNN, YOLOv5, YOLOv8, YOLOv10, RTMDet, RT-DETR, DEIM, and Mamba-YOLO. Results demonstrate that GLSNet achieves superior overall detection performance, with particularly notable advantages in resisting background-induced confusion under complex conditions and in recognizing small-scale targets, while maintaining high inference efficiency, thereby validating the effectiveness and synergy of the proposed modules.

Keywords：open-pit mine detection; state-space models (SSM); multi-scale features; global-local fusion.