标题：Extracting urban landmarks from geographical datasets using a random forests classifier

作者：Yue Lin, Yuyang Cai, Yue Gong, Mengjun Kang, Lin Li

来源出版物：International Journal of Geographical Information Science DOI: 10.1080/13658816.2019.1620238 出版时间：2019

摘要：Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods.

关键词：SMOTE, landmark salience, machine learning, spatial cognition, imbalanced dataset

地址：

[Yue Lin, Yue Gong, Mengjun Kang, Lin Li]School of Resource and Environmental Sciences, Wuhan University, Wuhan, China;

[Yue Lin, Yue Gong, Mengjun Kang, Lin Li]Institute of Smart Perception and Intelligent Computing, Wuhan University, Wuhan, China;

[Yuyang Cai]School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

通讯作者：Mengjun Kang; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China; Institute of Smart Perception and Intelligent Computing, Wuhan University, Wuhan, China

电子邮件地址:mengjunk@whu.edu.cn

影响因子：2.370