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.

本研究提出了一个堆叠多任务网络，在保持连通性正确性的同时，端到端地分割道路。在网络中，引入了全局感知模块来增强像素级道路特征表示并消除空中图像的干扰背景；增加了与道路方向相关的连通性任务，以确保网络保持道路片段的图级关系。我们还开发了一种堆叠多头结构，以共同学习和有效利用连通性学习和分割学习之间的相互信息。我们在三个公共遥感数据集上评估了所提出网络的性能。实验结果表明，该网络在道路分割准确性和连通性维护方面优于现有方法。

通过连接性和分割的联合学习进行道路网络的细粒度提取

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

Automated road network extraction from remote sensing imagery remains a
significant challenge despite its importance in a broad array of applications.
To this end, we explore road network extraction at scale with inference of
semantic features of the graph, identifying speed limits and route travel times
for each roadway. We call this approach City-Scale Road Extraction from
Satellite Imagery v2 (CRESIv2), Including estimates for travel time permits
true optimal routing (rather than just the shortest geographic distance), which
is not possible with existing remote sensing imagery based methods. We evaluate
our method using two sources of labels (OpenStreetMap, and those from the
SpaceNet dataset), and find that models both trained and tested on SpaceNet
labels outperform OpenStreetMap labels by greater than 60%. We quantify the
performance of our algorithm with the Average Path Length Similarity (APLS) and
map topology (TOPO) graph-theoretic metrics over a diverse test area covering
four cities in the SpaceNet dataset. For a traditional edge weight of geometric
distance, we find an aggregate of 5% improvement over existing methods for
SpaceNet data. We also test our algorithm on Google satellite imagery with
OpenStreetMap labels, and find a 23% improvement over previous work. Metric
scores decrease by only 4% on large graphs when using travel time rather than
geometric distance for edge weights, indicating that optimizing routing for
travel time is feasible with this approach.

本文提出了一种基于 CRESIv2 方法的城市级别道路提取和路线计算方法，该方法包含语义特征识别以及道路速度限制和行车时间的计算，旨在优化路线计算而非简单的最短路径距离。实验证明，该方法在使用 SpaceNet 数据集的训练和测试数据时优于使用 OpenStreetMap 标签的方法 60% 以上，并通过度量平均路径长度相似度（APLS）和地图拓扑（TOPO）等指标对算法的性能进行了评估。