{"title":"考虑周边特征的改进型基于 ANN 的地铁示意图标签放置方法","authors":"Zhiwei Wu, Tian Lan, Chenzhen Sun, Donglin Cheng, Xing Shi, Meisheng Chen, Guangjun Zeng","doi":"10.3390/ijgi13080294","DOIUrl":null,"url":null,"abstract":"On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"1 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved ANN-Based Label Placement Method Considering Surrounding Features for Schematic Metro Maps\",\"authors\":\"Zhiwei Wu, Tian Lan, Chenzhen Sun, Donglin Cheng, Xing Shi, Meisheng Chen, Guangjun Zeng\",\"doi\":\"10.3390/ijgi13080294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps.\",\"PeriodicalId\":48738,\"journal\":{\"name\":\"ISPRS International Journal of Geo-Information\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISPRS International Journal of Geo-Information\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/ijgi13080294\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISPRS International Journal of Geo-Information","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/ijgi13080294","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
在示意性地铁地图上,高质量的标签放置有助于乘客完成路线规划和定位任务。据报道,人工神经网络(ANN)可以利用学习到的标签知识来放置标签。然而,之前基于人工神经网络的方法只考虑了车站点及其连接边的影响。事实上,周围未连接的特征(点、边和标签)也会极大地影响标签放置的质量。为此,我们提出了一种改进的方法。首先,基于标注的自然智能(即制图师建立的标注经验、知识和规则)对标注位置与连接特征和周围特征之间的关系进行建模。然后,利用 ANN 学习这些关系。定量评估结果表明,与基准值(分别为 4.17%、14.29% 和 35.11%)相比,我们的方法达到了较低的标签点重叠率(0.00%)、标签边缘重叠率(4.12%)和标签与标签重叠率(20.58%)。另一方面,我们的方法有效地避免了模糊标签,并确保同一行的标签被放置在同一侧。定性评估显示,约 75% 的用户更喜欢我们的结果。这种新方法有望推动地铁示意图的自动生成。
An Improved ANN-Based Label Placement Method Considering Surrounding Features for Schematic Metro Maps
On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps.
期刊介绍:
ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.