Multimodal Location Based Services—Semantic 3D City Data as Virtual and Augmented Reality

Santana, J.J.; Wendel, Jochen; Trujillo, Agustín; Suárez, José P.; Simons, Alexander; Koch, Andreas

doi:10.1007/978-3-319-47289-8_17

Cited by 35 publications

(27 citation statements)

References 30 publications

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“…For example, SolarB performs city wide computations of solar radiation received by buildings' wall and roof surfaces [12], CityStats performs a socio-demographic clustering model for energy usage [13], CityBEM calculates monthly cooling and heating energy need of the buildings [6], heat demand and heat loss model calculate annual heat demand [11]. Furthermore, multiple visualization interfaces including 3D web mapping (three.js, Cesium), Augmented reality (AR), Virtual Reality (VR) and mobile 3D mapping have been realized (Glob3m API) [14][15]. This paper will focus on the generation of additional semantic 3D city models of other cities from free and open data that can be used by the above mentioned software infrastructure.…”

Section: Resultsmentioning

confidence: 99%

“…For LoD1 models, the average height value from the buffer analysis is taken to define the building height, for LoD2 models a ridgeline is identified in the LIDAR point cloud and averages values are taken along this ridge are taken to define the roof height and shape. Moreover, a custom 13 https://www.altergeosistemas.com/blog/2015/04/30/extrayendo-la-altura-de-losedificios-a-partir-de-datos-LIDAR/ script (genCityGML, available in GitHub 14 ) is developed to generate LoD1 and LoD2 CityGML standard from the 2D geometries by adding height information of ground and roof surfaces. The script is based on the Random3Dcity 15 tool developed by the TU Delft.…”

Section: Fig 3 Four Methods To Generate Citygml Data From Free and Omentioning

confidence: 99%

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Rapid development of semantic 3D city models for urban energy analysis based on free and open data sources and software

Wendel

Simons

Nichersu

et al. 2017

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

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Geospatial1 data, specifically semantic 3D building data, plays a crucial role in urban energy analysis as spatial calculations using 3D geometries usually form the basis for energy simulation and modelling needed for numerous smart cities applications. Additional information describing the building stock, such as building materials and energetic properties but also data not directly linked to urban morphology such as weather data, environmental data, vegetation or socio-demographic data sets are required for these applications. A major drawback in the widespread applicability of urban energy analysis is the lack of available data sets as well as the costly and lengthy labor-intensive process of generation of those data sets (e.g. 3D city models or LIDAR data). While recent years have seen an opening up of urban data sets through free and open data portals, web services, and APIs that are used for urban energy analysis, data standardization and varying data quality still raises big challenges. This research explores different methodologies for the generation and usage of semantic 3D city models based on free and open data sources and software. In this paper, we describe four different methodologies for the generation of semantic 3D city models from available open data (geospatial data portals, LIDAR data, Open Street Map data, and remote sensing data) and the tools required to achieve the task. To evaluate the suitability of these open-data sets for smart cities applications, multiple energy models, such as an energy performance model and a vertical solar radiation tool, previously developed in EIFER, have been applied to evaluate the applicability of these generated city models.

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Section: Resultsmentioning

confidence: 99%

Section: Fig 3 Four Methods To Generate Citygml Data From Free and Omentioning

confidence: 99%

Rapid development of semantic 3D city models for urban energy analysis based on free and open data sources and software

Wendel

Simons

Nichersu

et al. 2017

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

Self Cite

View full text Add to dashboard Cite

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“…Most underground-related applications are thought to be only useful in the context of AR and when designed for geological purposes (Lee et al [33]) or management of power and water underground utilities [3,27,29,34]. However, for multimodal applications we find the example of [35], in which a unique application features multiple environments, including virtual globes and VR or AR viewers. Their research shows that integrating underground visualization is still an open problem.…”

Section: Related Workmentioning

confidence: 99%

Making the Invisible Visible—Strategies for Visualizing Underground Infrastructures in Immersive Environments

Ortega

Wendel

Santana

et al. 2019

IJGI

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Visualization of underground infrastructure in an interactive 3D immersive environment is extremely important for efficient management of city’s infrastructure. This paper describes different geometric modelling approaches to illustrate appropriate visualization of such data. A multimodal prototype has been developed by exploiting different algorithms to render these invisible underground objects as part of an urban model. This prototype has been integrated in an immersive geographic information system (GIS), named MultiVis, for handheld iOS and Android devices. As a part of the study, three distinct strategies have been tested; the first is based on the use of transparencies to convey a sense of depth, the second relies on an image-space superposition of “ditches” on top of the rendered frame and the third is a world-space deformation of the elevation model that exposes the underground elements. Furthermore, a comparative user experience analysis of different techniques aimed to the geometrically accurate visualisation of utility networks and other underground facilities are performed and evaluated. It includes a set of user evaluations for different parameters of these techniques, which gives us an insight on how the proposed methods affect the experience and usability for technical and non-technical users.

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“…Handheld accelerometers are known as inertial sensors as they are allowed to exploit the property of inertia, for example, the resistance to a change in momentum in AR environment, to sense angular motion in the case of depth camera sensing and mobile changes in linear motion [10]. Furthermore, inclinometers are also inertial sensors that measure the orientation of the acceleration vector due to gravity [11]. Besides, this paper defines inertial sensors as independent of any external references or infrastructure, apart from the ubiquitous gravity field.…”

Section: Real Environment Ar Environmentmentioning

confidence: 99%

An Inertial Device-based User Interaction with Occlusion-free Object Handling in a Handheld Augmented Reality

Goh¹,

Sunar²,

Ismail³

et al. 2018

IJIE

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Multimodal Location Based Services—Semantic 3D City Data as Virtual and Augmented Reality

Cited by 35 publications

References 30 publications

Rapid development of semantic 3D city models for urban energy analysis based on free and open data sources and software

Rapid development of semantic 3D city models for urban energy analysis based on free and open data sources and software

Making the Invisible Visible—Strategies for Visualizing Underground Infrastructures in Immersive Environments

An Inertial Device-based User Interaction with Occlusion-free Object Handling in a Handheld Augmented Reality

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