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Bridge detection in remote sensing images (RSIs) plays a crucial role invarious applications, but it poses unique challenges compared to the detectionof other objects. In RSIs, bridges exhibit considerable variations in terms oftheir spatial scales and aspect ratios. Therefore, to ensure the visibility andintegrity of bridges, it is essential to perform holistic bridge detection inlarge-size very-high-resolution (VHR) RSIs. However, the lack of datasets withlarge-size VHR RSIs limits the deep learning algorithms' performance on bridgedetection. Due to the limitation of GPU memory in tackling large-size images,deep learning-based object detection methods commonly adopt the croppingstrategy, which inevitably results in label fragmentation and discontinuousprediction. To ameliorate the scarcity of datasets, this paper proposes alarge-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled fromdiverse geographic locations across the globe. These images encompass a widerange of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, andcollectively feature 59,737 bridges. Furthermore, we present an efficientnetwork for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Netpresents a separate detector-based feature fusion (SDFF) architecture and isoptimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based onthe proposed GLH-Bridge dataset, we establish a bridge detection benchmarkincluding the OBB and HBB tasks, and validate the effectiveness of the proposedHBD-Net. Additionally, cross-dataset generalization experiments on two publiclyavailable datasets illustrate the strong generalization capability of theGLH-Bridge dataset.

Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery