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

TL;DR

This paper introduces a novel multitask neural network that simultaneously segments roads and preserves their connectivity in satellite images, improving accuracy and structural integrity for applications like autonomous driving.

Contribution

It proposes a stacked multitask network with a global-aware module and connectivity task, enhancing road segmentation and connectivity preservation in remote sensing images.

Findings

Outperforms state-of-the-art methods in accuracy

Effectively preserves road connectivity in segmentation

Validated on three public datasets

Abstract

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.