摘要
道路选取一直是制图综合领域的重要研究内容,针对现有方法仅考虑单一层次选取单元特征等问题,本文提出一种融合路段和stroke特征的道路自动选取方法。首先,以路段和stroke为基本单元,构建对偶图表达路网的拓扑结构;然后,将路段的几何、类型等级和图连通性指标作为路段特征,将长度、包含路段数量、同一stroke下路段的连接数量作为stroke特征,融合stroke特征到对应的路段单元上;接着,将上述求得的特征矩阵输入到GraphSAGE模型中进行学习,输出路段节点的分类结果;最后提出顾及stroke连贯性的增加最小节点数方法保持路网的连通性,进而完成道路的选取。本文采用河南省郑州市1∶25万和1∶50万比例尺的路网数据进行实验,结果表明:(1)本文方法能有效聚合路段和stroke的特征,相较于文献[17]的方法和仅考虑基本特征组合的以路段或stroke为选取单元的对比方法,模型预测准确率提升了6.36%、7.36%、3.13%;(2)本文提出的连通性保持算法处理后的结果往往更符合道路选取的认知规律,也能进一步提升选取的准确率;(3)完成连通性处理后,本文方法相较两种对比方法在一致道路长度上提升了125.45 km、110.438 km,在一致道路数量占比上提升了8.72%、20.43%,同时能更好保持路网整体格局和局部关键结构以及密度分布。与现有的道路选取方法相比,本文方法能更好地利用多层次道路特征进而提升道路选取效果,为制图综合、级联更新等领域的后续研究提供一种新的思路。
Road selection has always been a significant research aspect of cartographic generalization,which is of great significance for spatial data linkage updating and multi-scale representation.The existing selection methods mainly include those based on stroke,semantic information,graph theory,road density,and artificial intelligence,but they only consider the features of a single level selection unit.Therefore,this paper proposes an automatic road selection method that integrates road segment and stroke features.Firstly,the road segment and stroke are used as basic units to construct a dual graph representing the spatial structure of the road network.Based on this,feature calculations are performed:length,degree,closeness centrality,betweenness centrality,and hierarchy are considered as road segment features,while length,the number of containing road segments,and the number of connections of road segment under the same stroke are regarded as stroke features.These stroke features are then integrated into the corresponding road segment unit.The obtained feature matrix is input into the GraphSAGE model for learning,which outputs the classification result of road segment.Finally,a method that increases the minimum number of nodes while considering stroke coherence is utilized to maintain the connectivity of the road network,thereby completing the road selection.Experiments were conducted using 1:250000 and 1:500000 scale road network data from Zhengzhou,Henan Province.The results indicate that the proposed method effectively integrates the features of road segments and strokes,overcoming the limitations of using a single road segment or stroke as the selection unit.Compared to the method in reference 17 and the comparative methods that use a road segment or stroke as the selection unit,the model's prediction accuracy improved by 6.36%,7.36% and 3.13%,respectively.The results processed by the proposed connectivity preservation algorithm were more in line with the cognitive rules of road selection and could further improve the accuracy of selection.After connectivity processing,the proposed method improved the consistent road length by 125.45 km and 110.438 km,and the proportion of consistent road numbers by 8.72% and 20.43%,respectively,while better maintaining the overall and local key structures and density distribution of the road network.Compared with existing road selection methods,this method can better utilize multi-level road features to improve the effectiveness of road selection,providing a new approach for subsequent research in areas such as cartographic generalization and linkage updating.
作者
赵天明
孙群
马京振
张付兵
温伯威
ZHAO Tianming;SUN Qun;MA Jingzhen;ZHANG Fubing;WEN Bowei(Information Engineering University,Zhengzhou 450052,China;Collaborative Innovation Center of Geo-information Technology for Smart Central Plains,Henan Province,Zhengzhou 450052,China;Key Laboratory of Spatiotemporal Perception and Intelligent processing,Ministry of Natural Resources,Zhengzhou 450052,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2024年第12期2673-2685,共13页
Journal of Geo-information Science
基金
国家自然科学基金项目(42101455、42101454)。
