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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Urban road mapping based on an end-to-end road vectorization mapping network framework
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Urban road mapping based on an end-to-end road vectorization mapping network framework

机译：基于端到端道路矢量化映射网络框架的城市道路映射

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Reliable urban road vector maps are essential for urban analysis because the spatial distribution of road networks reflects urban development under the combined effects of nature and socio-economics. Diverse very high resolution (VHR) remote sensing images are now available, enabling explicit extraction of urban road vector maps over wide areas. Urban road vectorization mapping consists of two separate tasks: road extraction and road vectorization. The traditional methods focus on the road extraction task, and can obtain a good performance when using a pixel-based metric. However, the road vectorization methods are faced with the problem of road connectivity. In this work, to implement urban road vectorization mapping in a unified way, an end-to-end road vectorization mapping network (RVMNet) framework is proposed. The proposed RVMNet framework consists of a node proposal network (NPN) module and a node connectivity based road refinement module. In the NPN module, a fully convolutional network is adopted for the road node extraction. This improves the connectivity of the road mask by providing supervised information in the form of the road nodes, which are actually part of the road mask. The road mask is then converted into a road vector map by vectorization. In the node connectivity based road refinement module, road nodes are inserted into the road vector map to improve the connectivity. We compared RVMNet with the other state-of-the-art road detection methods on two public road datasets (SpaceNet 3 and DeepGlobe). The results of this comparison showed that combining road extraction and road vectorization into a unified framework is an efficient and accurate strategy for urban road vectorization mapping because it can propose road nodes that help to improve the road connectivity. Moreover, we constructed the novel UrbanRoadNet dataset, covering six cities (Beijing city center; Helsinki; Wuhan; Macao; the Wan Chai area of Hong Kong; and Shanghai). We then applied the RVMNet framework to the data from the six cities, obtaining an improvement in the vector-based average path length similarity (APLS) value of 4.1%. The spatial transfer assessments from both the qualitative and quantitative aspects corroborated the robust generalizability of the proposed method, and further verified the effectiveness of the proposed approach for large-scale road vectorization mapping at a very high resolution. It was also found that road vector spatial distribution is a useful way to reflect urban development.
机译：可靠的城市道路向量地图对城市分析至关重要，因为道路网络的空间分布在自然和社会经济的综合影响下反映了城市发展。现在可以使用多种高分辨率（VHR）遥感图像，从而可以明确提取城市道路向量地图。城市道路矢量化映射由两个单独的任务组成：道路提取和道路矢量化。传统方法侧重于道路提取任务，并在使用基于像素的度量时可以获得良好的性能。但是，道路矢量化方法面临着道路连接的问题。在这项工作中，为了以统一的方式实现城市道路矢量化映射，提出了端到端的道路矢量化映射网络（RVMNET）框架。所提出的RVMNET Framework包括节点提案网络（NPN）模块和基于节点连接的道路改进模块。在NPN模块中，采用完全卷积的网络进行道路节点提取。这通过提供道路节点形式的监督信息来改善道路掩模的连接，这实际上是道路掩模的一部分。然后通过矢量化转换为道路矢量地图。在基于节点连接的道路细化模块中，路节点插入到道路矢量地图中以提高连接。我们将RVMNET与两个公共道路数据集（Spacenet 3和Deepglobe）进行了其他最先进的道路检测方法。该比较的结果表明，将道路提取和路向导中的道路矢量化结合到统一框架中，是一种高效准确的城市道路矢量化映射，因为它可以提出有助于提高道路连接的道路节点。此外，我们构建了新颖的UrbanRoadnet数据集，涵盖了六个城市（北京市中心;赫尔辛基;武汉;澳门;湾仔区;和上海）。然后，我们将RVMNet Framework应用于六个城市的数据，从而改善了基于向量的平均路径长度相似性（APL）值为4.1％。定性和定量方面的空间转移评估证实了所提出的方法的鲁棒概括性，并进一步验证了在非常高分辨率上进行大规模道路矢量化映射的提出方法的有效性。还发现道路向量空间分布是反映城市发展的有用方式。

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