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Xuke Hu
GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany

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Journal article
Published: 28 June 2019 in Remote Sensing
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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).

ACS Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Alexander Zipf; Zhiyong Wang; Jianga Shang. Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing 2019, 11, 1535 .

AMA Style

Xuke Hu, Hongchao Fan, Alexey Noskov, Alexander Zipf, Zhiyong Wang, Jianga Shang. Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing. 2019; 11 (13):1535.

Chicago/Turabian Style

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

Articles
Published: 12 September 2017 in International Journal of Digital Earth
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Currently, very few roof shape information for complex buildings is available on OSM. Moreover, additional data requirements (e.g. 3D point clouds) limit the applicability of many roof reconstruction approaches. To mitigate this issue, we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints: the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints. First, it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm. Second, a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions. Then, a set of selection rules are defined to rank partitions, and the best ones are chosen for roof shape recommendation. Finally, a set of combination rules and a symmetry rule are defined. It enables to evaluate the probability of a footprint being a certain combination of roof shapes. Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17–45% and from a prior probability of 0.29–14.3%, removing 60% and 93% of the incorrect roof shape options, respectively.

ACS Style

Xuke Hu; Hongchao Fan; Alexey Noskov. Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints. International Journal of Digital Earth 2017, 11, 1039 -1063.

AMA Style

Xuke Hu, Hongchao Fan, Alexey Noskov. Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints. International Journal of Digital Earth. 2017; 11 (10):1039-1063.

Chicago/Turabian Style

Xuke Hu; Hongchao Fan; Alexey Noskov. 2017. "Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints." International Journal of Digital Earth 11, no. 10: 1039-1063.

Journal article
Published: 19 June 2017 in Sustainability
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As it is widely accepted, cycling tends to produce health benefits and reduce air pollution. Policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems (BSS). It is increasingly interesting to investigate how environmental factors influence the cycling behavior of users of bicycle-sharing systems, as users of bicycle-sharing systems tend to be different from regular cyclists. Although earlier studies have examined effects of safety and convenience on the cycling behavior of regular riders, they rarely explored effects of safety and convenience on the cycling behavior of BSS riders. Therefore, in this study, we aimed to investigate how road safety, convenience, and public safety affect the cycling behavior of BSS riders by controlling for other environmental factors. Specifically, in this study, we investigated the impacts of environmental characteristics, including population density, employment density, land use mix, accessibility to point-of-interests (schools, shops, parks and gyms), road infrastructure, public transit accessibility, road safety, convenience, and public safety on the usage of BSS. Additionally, for a more accurate measure of public transit accessibility, road safety, convenience, and public safety, we used spatiotemporally varying measurements instead of spatially varying measurements, which have been widely used in earlier studies. We conducted an empirical investigation in Chicago with cycling data from a BSS called Divvy. In this study, we particularly attempted to answer the following questions: (1) how traffic accidents and congestion influence the usage of BSS; (2) how violent crime influences the usage of BSS; and (3) how public transit accessibility influences the usage of BSS. Moreover, we tried to offer implications for policies aiming to increase the usage of BSS or for the site selection of new docking stations. Empirical results demonstrate that density of bicycle lanes, public transit accessibility, and public safety influence the usage of BSS, which provides answers for our research questions. Empirical results also suggest policy implications that improving bicycle facilities and reducing the rate of violent crime rates tend to increase the usage of BSS. Moreover, some environmental factors could be considered in selecting a site for a new docking station.

ACS Style

Yeran Sun; Amin Mobasheri; Xuke Hu; Weikai Wang. Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users. Sustainability 2017, 9, 1060 .

AMA Style

Yeran Sun, Amin Mobasheri, Xuke Hu, Weikai Wang. Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users. Sustainability. 2017; 9 (6):1060.

Chicago/Turabian Style

Yeran Sun; Amin Mobasheri; Xuke Hu; Weikai Wang. 2017. "Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users." Sustainability 9, no. 6: 1060.

Journal article
Published: 15 December 2016 in Sensors
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Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity.

ACS Style

Jianga Shang; Xuke Hu; Wen Cheng; Hongchao Fan. GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors. Sensors 2016, 16, 2137 .

AMA Style

Jianga Shang, Xuke Hu, Wen Cheng, Hongchao Fan. GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors. Sensors. 2016; 16 (12):2137.

Chicago/Turabian Style

Jianga Shang; Xuke Hu; Wen Cheng; Hongchao Fan. 2016. "GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors." Sensors 16, no. 12: 2137.