Road Network Selection

The source codes that support the paper 'Automatic road network selection method considering functional semantic features of roads with graph convolutional networks' published in the International Journal of Geographical Information Science.Abstract: Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e., travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography.Keywords: road network selection; graph convolutional network; functional features; map generalization; POI data本数据集为发表于《国际地理信息科学期刊》（International Journal of Geographical Information Science）的论文《融合图卷积网络与道路功能语义特征的道路网自动选取方法》（Automatic road network selection method considering functional semantic features of roads with graph convolutional networks）提供支撑源代码。摘要：道路网选取是构建多尺度道路网地图的地图综合（map generalization）核心环节。现有方法通常仅基于道路的几何与拓扑特征判定道路重要性，极少结合人类出行数据从道路使用效率维度评估道路重要性，忽视了道路的功能价值，导致生成结果与实际出行需求不匹配。本文提出两类功能语义特征——出行路径选择概率与区域吸引力，用以量化道路功能重要性，并构建了一种基于图卷积网络（GCN, graph convolutional networks）的道路网自动选取方法，将道路网选取任务建模为二分类问题。首先，构建表征原始道路网的对偶图，并提取道路特征：包含6类图形特征与2类功能语义特征。随后，设计带有连通性损失函数的扩展图卷积网络模型以生成多尺度道路网，并提出基于道路连续性原则的优化策略以保障道路拓扑结构的合理性。实验结果表明，引入功能特征的所提模型可有效提升道路网选取结果的质量，尤其适用于中、大比例尺地图。本方法相较于当前主流方法性能更优，为人工智能模型赋能制图领域提供了极具价值的探索尝试。关键词：道路网选取；图卷积网络（GCN, graph convolutional network）；功能特征；地图综合；兴趣点（POI, Point of Interest）数据