Extracting urban landmarks from geographical datasets using a random forests classifier. Issue 12 (2nd December 2019)

• Record Type: Journal Article
• Authors: Lin, Yue; Cai, Yuyang; Gong, Yue; Kang, Mengjun; Li, Lin
• Is Part Of: International journal of geographical information science. Volume 33:Issue 12(2019)
• Page Start: 2406
• Page End: 2423
• Publication Date: 2019-12-02
• Subjects: SMOTE -- landmark salience -- machine learning -- spatial cognition -- imbalanced dataset; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1620238
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: 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.