Convolutional neural networks (CNN) have made significant advances in detecting roads from satellite images. However, existing CNN approaches are generally repurposed semantic segmentation architectures and suffer from the poor delineation of long and curved regions. Lack of overall road topology and structure information further deteriorates their performance on challenging remote sensing images. This paper presents a novel multi-task graph neural network (GNN) which simultaneously detects both road regions and road borders; the inter-play between these two tasks unlocks superior performance from two perspectives: (1) the hierarchically detected road borders enable the network to capture and encode holistic road structure to enhance road connectivity (2) identifying the intrinsic correlation of semantic landcover regions mitigates the difficulty in recognizing roads cluttered by regions with similar appearance. Experiments on challenging dataset demonstrate that the proposed architecture can improve the road border delineation and road extraction accuracy compared with the existing methods.

翻译：卷积神经网络（CNN）在从卫星图像中检测道路方面取得了显著进展。然而，现有的CNN方法通常是对语义分割架构的再利用，在描绘长而弯曲的区域方面表现不佳。缺乏对道路整体拓扑和结构信息的把握进一步降低了这些方法在具有挑战性的遥感图像上的性能。本文提出了一种新颖的多任务图神经网络（GNN），它同时检测道路区域和道路边界；这两个任务之间的相互作用从两个角度释放了卓越的性能：（1）分层检测到的道路边界使网络能够捕获并编码整体道路结构，从而增强道路连通性；（2）识别语义土地覆盖区域的内在相关性，缓解了识别被外观相似区域所干扰的道路的难度。在具有挑战性的数据集上的实验表明，与现有方法相比，所提出的架构能够改善道路边界的描绘和道路提取的准确性。