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Open AccessArticle

Feasibility of Using Grammars to Infer Room Semantics

1
GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany
2
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway
3
Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, 3584 Utrecht, The Netherlands
4
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
5
National Engineering Research Center for Geographic Information System, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1535; https://doi.org/10.3390/rs11131535
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)
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Abstract

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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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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.

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