PSeq-Net: End-to-End Sparse-Global Sequence Modeling for Bridge Detection in Ultra-High-Resolution Remote Sensing Imagery

Robust bridge detection in ultra-high-resolution remote sensing imagery is important for transportation infrastructure mapping, geographic information system updating, and post-disaster assessment. However, local cropping can interrupt the spatial continuity of long-span bridges, leading to fragmented detections and geometrically inconsistent predictions. This paper proposes a polygon sequence network (PSeq-Net), an end-to-end sparse-global sequence modeling framework for bridge detection in very-high-resolution (VHR) remote sensing imagery. PSeq-Net generates an ordered quadrilateral vertex sequence for each bridge, unifying visual encoding and structured geometric prediction. Hierarchical sparse feature sampling preserves fine-grained evidence in bridge-relevant regions while using compact representations of background areas, enabling efficient processing of 2048 × 2048 inputs. Global context aggregation integrates long-range spatial dependencies, and geometry-aware optimization improves the structural plausibility of predicted bridge shapes. Experiments on the large-scale GLH-Bridge benchmark show that PSeq-Net achieves 37.87% average precision (AP), 73.58% AP50, and 31.38% AP75, outperforming representative oriented and large-scale bridge detectors. Further analyses highlight improved localization of elongated and boundary-crossing bridges, reduced fragmentation, and robustness across diverse scene backgrounds and image resolutions.