Next Article in Journal
Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images
Next Article in Special Issue
An Accurate Visual-Inertial Integrated Geo-Tagging Method for Crowdsourcing-Based Indoor Localization
Previous Article in Journal
Characteristics of Absorption Spectra of Chromophoric Dissolved Organic Matter in the Pearl River Estuary in Spring
Previous Article in Special Issue
A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

Feasibility of Using Grammars to Infer Room Semantics

GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, 3584 Utrecht, The Netherlands
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
National Engineering Research Center for Geographic Information System, Wuhan 430074, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1535;
Received: 13 May 2019 / Revised: 14 June 2019 / Accepted: 26 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue Mobile Mapping Technologies)
Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging). View Full-Text
Keywords: indoor mapping; room type tagging; semantic enrichment; grammar; Bayesian inference indoor mapping; room type tagging; semantic enrichment; grammar; Bayesian inference
Show Figures

Figure 1

MDPI and ACS Style

Hu, X.; Fan, H.; Noskov, A.; Zipf, A.; Wang, Z.; Shang, J. Feasibility of Using Grammars to Infer Room Semantics. Remote Sens. 2019, 11, 1535.

AMA Style

Hu X, Fan H, Noskov A, Zipf A, Wang Z, Shang J. Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing. 2019; 11(13):1535.

Chicago/Turabian Style

Hu, Xuke, Hongchao Fan, Alexey Noskov, Alexander Zipf, Zhiyong Wang, and Jianga Shang. 2019. "Feasibility of Using Grammars to Infer Room Semantics" Remote Sensing 11, no. 13: 1535.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop