Paper Info
Title
Tagging The Main Entrances Of Public Buildings Based On Openstreetmap And Binary Imbalanced Learning
Abstract
Determining the location of a building's entrance is crucial to location-based services, such as wayfinding for pedestrians. Unfortunately, entrance information is often missing from current mainstream map providers such as Google Maps. Frequently, automatic approaches for detecting building entrances are based on street-level images that are not widely available. To address this issue, we propose a more general approach for inferring the main entrances of public buildings based on the association between spatial elements extracted from OpenStreetMap. In particular, we adopt three binary classification approaches, weighted random forest, balanced random forest, and smooth-boost to model the association relationship. There are two types of features considered in the classification: intrinsic features derived from building footprints and extrinsic features derived from spatial contexts, such as roads, green spaces, bicycle parking areas, and neighboring buildings. We conducted extensive experiments on 320 public buildings with an average perimeter of 350 m. The experimental results showed that the locations of building entrances estimated by the weighted random forest and balanced random forest models have a mean linear distance error of 21 m and a mean path distance error of 22 m, ruling out 90% of the incorrect locations of the main entrance of buildings.
Year
DOI
Venue
2021
10.1080/13658816.2020.1861282
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
DocType
Volume
Main entrance tagging, OpenStreetMap, imbalanced learning, random forest
Journal
35
Issue
ISSN
Citations 
9
1365-8816
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Xuke Hu173.82
Alexey Noskov202.70
Hongchao Fan3177.44
Tessio Novack410.69
Hao Li526185.92
Fuqiang Gu6152.21
Jianga Shang7152.21
alexander zipf822923.84