微地图作为自媒体时代出现的新型地图,具有大众参与、个性化、快速传播等特征,然而现有微地图研究在点符号设计方面存在一定局限,难以完全满足大众个性化制图的需求.为解决这一问题,本文从微地图制作角度出发,选择手绘地图的通用地图符号作为研究对象,构建了一个包含多种类型和样式的手绘地图数据集.在现有研究的基础上,通过对比选择目标检测中常用的YOLOv5(You Only Look Once v5)系列模型,深入探索手绘地图中通用地图符号的自动提取方法,并采用YOLOv5-X模型进行手绘地图通用地图符号的提取.实验结果显示,该模型在手绘地图数据集上的point类别提取精确度、召回率和F1得分分别达到了98.42％、94.72％和97％.同时在Quick Draw涂鸦数据集上进行模型泛化能力的测试,本文所使用的模型在该数据集上展现出良好的提取效果.本研究的开展不仅扩充了微地图个性化点符号的研究数据集,还改进了通用地图符号的提取方法,为微地图制图注入了更多元化的元素,也为自媒体时代的地图制作提供了更为灵活和个性化的解决方案.
Automatic Extraction Method of Point Symbols in Modern Hand-Drawn Maps for We-Map
The We-Map,a novel cartographic phenomenon emerging in the era of social media,is distinctively characterized by mass participation,personalization,and swift dissemination. However,existing research on We-Map falls short in addressing the intricate challenges posed by point symbol design,thereby hampering the fulfillment of the public's desire for personalized cartographic representations. To bridge this gap,this paper starts from the perspective of We-Map mapping production,taking common map symbols in hand-drawn maps as the research object,and constructs an open hand-drawn map dataset. To this end,we have constructed a comprehensive dataset encompassing a diverse array of hand-drawn map symbols,encompassing various types and styles. This dataset serves as a valuable resource for exploring and enhancing the automated extraction of common map symbols. Drawing inspiration from existing research,we have embarked on a journey to identify and evaluate the most suitable model for our task. Among the numerous models for object detection,the performance of the YOLOv5 series models is well-known,and therefore this article will not delve into it excessively. Specifically,through comparison,we ultimately chose the YOLOv5-X model,which boasts advanced capabilities in object detection and classification. By leveraging the YOLOv5-X model,we have achieved remarkable results in the automatic extraction of common map symbols from hand-drawn maps. Our experiments reveal that the model achieves high levels of accuracy,recall,and F1 score in identifying and extracting point categories from the hand-drawn map dataset. These scores stand testament to the model's effectiveness in capturing the intricate details and unique characteristics of hand-drawn map symbols. Moreover,to further validate the generalizability of our model,we have conducted additional experiments on the Quick Draw doodle dataset. The results obtained from these experiments confirm that our model performs equally well in extracting common map symbols from diverse and varying datasets. The significance of this study lies not only in enhancing the dataset available for personalized point symbol research in We-Map but also in advancing the techniques for extracting common map symbols. By introducing more diversified elements into We-Map cartography,we have opened up new avenues for more flexible and personalized mapmaking in the age of self-media. This study represents a significant step forward in the evolution of cartography in the rea of self-media,catering to the evolving needs and preferences of the modern audience. The finally extracted point symbols can provide a data foundation for downstream tasks related to We-Map.
common map symbolhand-drawn maphand-drawn map datasetWe-MapWe-Map symbol librarypoint symbol extractionYOLOv5-X
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