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Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning

Hu, Xuke and Alexey, Noskov and Fan, Hongchao and Novack, Tessio and Li, Hao and Gu, Fuqiang and Shang, Jianga and Zipf, Alexander (2020) Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning. International Journal of Geographical Information Science. Taylor & Francis. ISSN 1365-8816. (In Press)

[img] PDF - Postprint version (accepted manuscript)


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. The proposed approach indicates a marked improvement to automatically locate building entrances in support for location-based services, such as indoor-outdoor navigation and deliveries.

Item URL in elib:https://elib.dlr.de/140288/
Document Type:Article
Title:Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hu, XukeXuke.Hu (at) dlr.deUNSPECIFIED
Alexey, NoskovPhilipps University of MarburgUNSPECIFIED
Fan, Hongchaohongchao.fan (at) ntnu.noUNSPECIFIED
Novack, TessioHeidelberg UniversityUNSPECIFIED
Li, HaoHeidelberg UniversityUNSPECIFIED
Gu, FuqiangNational University of SingaporeUNSPECIFIED
Shang, JiangaChina University of GeosciencesUNSPECIFIED
Zipf, Alexanderzipf (at) uni-heidelberg.deUNSPECIFIED
Journal or Publication Title:International Journal of Geographical Information Science
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Publisher:Taylor & Francis
Status:In Press
Keywords:Main entrance tagging;OpenStreetMap; Imbalanced learning; Random forest
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Communication, Navigation and Surveillance (old)
Location: Jena
Institutes and Institutions:Institute of Data Science
Institute of Data Science > Citizen Science
Deposited By: Hu, Xuke
Deposited On:14 Jan 2021 13:17
Last Modified:14 Jan 2021 13:17

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