Learning to Holistically Detect Bridges from Large-Size VHR Remote
  Sensing Imagery
Yansheng Li Junwei Luo Yongjun Zhang Yihua Tan Jin-Gang Yu Song Bai
Bridge detection in remote sensing images (RSIs) plays a crucial role in
various applications, but it poses unique challenges compared to the detection
of other objects. In RSIs, bridges exhibit considerable variations in terms of
their spatial scales and aspect ratios. Therefore, to ensure the visibility and
integrity of bridges, it is essential to perform holistic bridge detection in
large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with
large-size VHR RSIs limits the deep learning algorithms' performance on bridge
detection. Due to the limitation of GPU memory in tackling large-size images,
deep learning-based object detection methods commonly adopt the cropping
strategy, which inevitably results in label fragmentation and discontinuous
prediction. To ameliorate the scarcity of datasets, this paper proposes a
large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from
diverse geographic locations across the globe. These images encompass a wide
range of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, and
collectively feature 59,737 bridges. Furthermore, we present an efficient
network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net
presents a separate detector-based feature fusion (SDFF) architecture and is
optimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based on
the proposed GLH-Bridge dataset, we establish a bridge detection benchmark
including the OBB and HBB tasks, and validate the effectiveness of the proposed
HBD-Net. Additionally, cross-dataset generalization experiments on two publicly
available datasets illustrate the strong generalization capability of the
GLH-Bridge dataset.