Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation
This work addresses road network extraction for applications like intelligent traffic management and autonomous driving, representing an incremental improvement with a novel hybrid method.
The paper tackled the challenge of extracting road networks from high-resolution satellite images by proposing a stacked multitask network that jointly learns segmentation and connectivity, resulting in improved accuracy and connectivity maintenance compared to state-of-the-art methods on three public datasets.
Road network extraction from satellite images is widely applicated in intelligent traffic management and autonomous driving fields. The high-resolution remote sensing images contain complex road areas and distracted background, which make it a challenge for road extraction. In this study, we present a stacked multitask network for end-to-end segmenting roads while preserving connectivity correctness. In the network, a global-aware module is introduced to enhance pixel-level road feature representation and eliminate background distraction from overhead images; a road-direction-related connectivity task is added to ensure that the network preserves the graph-level relationships of the road segments. We also develop a stacked multihead structure to jointly learn and effectively utilize the mutual information between connectivity learning and segmentation learning. We evaluate the performance of the proposed network on three public remote sensing datasets. The experimental results demonstrate that the network outperforms the state-of-the-art methods in terms of road segmentation accuracy and connectivity maintenance.