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Road network extraction from satellite images is widely applicated inintelligent traffic management and autonomous driving fields. Thehigh-resolution remote sensing images contain complex road areas and distractedbackground, which make it a challenge for road extraction. In this study, wepresent a stacked multitask network for end-to-end segmenting roads whilepreserving connectivity correctness. In the network, a global-aware module isintroduced to enhance pixel-level road feature representation and eliminatebackground distraction from overhead images; a road-direction-relatedconnectivity task is added to ensure that the network preserves the graph-levelrelationships of the road segments. We also develop a stacked multiheadstructure to jointly learn and effectively utilize the mutual informationbetween connectivity learning and segmentation learning. We evaluate theperformance of the proposed network on three public remote sensing datasets.The experimental results demonstrate that the network outperforms thestate-of-the-art methods in terms of road segmentation accuracy andconnectivity maintenance.

Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation