Spatial Analysis of Environmental Hazards

Spatial Analysis of Environmental Hazards

designing and implementing a 3D indoor navigation web application

Authors
Kharazmi university
Abstract
Designing and implementing a 3D indoor navigation web application

Extended abstract

Nowadays, due to the complexity of interior space of buildings, the need arises for indoor navigation inside such spaces. Indoor navigation systems may be helpful for emergency evacuation of the crowd in natural hazards such as earthquake as well as human-made disasters. These systems can also act as a decision support system for officials. Literature survey on indoor navigation services shows that a large number of researches have been conducted around designing and implementing such systems but automatic indoor spaces topology extraction of the current building information models remains as a challenge. This research aims to introduce, design and implement a web-based indoor navigation system using CityGML data model in LOD4 (level of detail) to overcome the mentioned problem.

The architecture of the current research is a browser-based web application service such that the data model processing and graph creation is implemented on the server side, the client interface and calculated path are represented on the client side (browser). Through the CityGML data model processing, firstly, the building navigable spaces such as room floor, doors and stairs are extracted and then, each space as a node and the connections between the nodes are defined as edges, are imported to the navigation graph. Programming on the server side has been performed by Python language and web development languages including HTML (Hypertext Markup language), JavaScript, JQuery and AJAX are used on the client side. Cesium virtual globe has been exploited to display the data model and the calculated route.

To evaluate the introduced methodology and designed service, a three floor house with CityGML format in LOD4 was used as the case study. Generally, a client can request a 3D calculated path by selecting the source and destination points on the client browser. The server receives the request and returns the response as a 3D line to the client browser on the Cesium environment. In addition, a descriptive graphical user interface for visual inception of the route is offered to the users on their browser.

One of the advantages of the designed web application is that, the service is implemented on the browser. Hence, all devices equipped with a browser have possibility to run the 3D routing service. Besides the mentioned cross-platform capability, average expectation time of the graphical interface loading, data module processing and path finder module are 7.03 milliseconds, 12.42 seconds and 2.44 seconds respectively that visits a valuable criteria in emergency situations like an earthquake phenomenon. Regarding this fact that CityGML is a new data model and supported by a few software, the introduced architecture causes less implementation costs as well as automation of these systems.



Keywords: 3D indoor navigation, web application, interior space of buildings
Keywords

Afyouni I, Ray C, Christophe C. (2012). Spatial models for context-aware indoor navigation systems: A survey. Journal of Spatial Information Science, 4(4), 85–123.
Boguslawski P, Gold C M, Ledoux H. (2011). Modelling and analysing 3D buildings with a primal/dual data structure. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 188-197.
Boguslawski P, Mahdjoubi L, Zverovich V, Fadli F. (2016). Automated construction of variable density navigable networks in a 3D indoor environment for emergency response. Automation in Construction, 72(2), 115-128.
Cesium. (2019, Jan 19). Retrieved from https://cesiumjs.org/
Diakite´ A A, Zlatanova S, Li K J. (2017). ABOUT THE SUBDIVISION OF INDOOR SPACES IN INDOORGML. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial, IV-4/W5, 41–48.
Goetz, M. (2012). Using Crowdsourced Indoor Geodata for the Creation of a Three-Dimensional Indoor Routing Web Application. Future Internet, 4, 575-591.
Isikdag U, Zlatanova S, Underwood J. (2013). A BIM-Oriented Model for supporting indoor navigation requirements. Computers, Environment and Urban Systems, 41, 112-123.
Jamali A, Abdul Rahman A, Boguslawski P, Kumar P, Gold C M. (2017). An automated 3D modeling of topological indoor navigation network. GeoJournal, 82(1), 157–170.
Khan A A, Donaubauer A, Kolbe T H. (2014). A multi-step transformation process for automatically generating indoor routing graphs from existing semantic 3D building models. 9th 3DGeoInfo Conference. Germany: Conference Chairs of 3DGeoInfo.
Kim K H, Wilson J P. (2015). Planning and visualising 3D routes for indoor and outdoor spaces using CityEngine. Journal of Spatial Science, 60(1), 179-193.
Kolbe, T. H. (2008). Representing and Exchanging 3D City Models with CityGML. Lecture Notes in Geoinformation and Cartography, 15-31.
Krūminaitė M, Zlatanova S. (2014). Indoor space subdivision for indoor navigation. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, (pp. 25-31). Dallas.
Lee, J. (2004). A Spatial Access-oriented Implementation of a 3-D GIS Topological Data Model for Urban Entities. GeoInformatica, 8(3), 237-264.
Liu, L. (2017). Indoor Semantic Modelling for Routing, The Two-Level Routing Approach for Indoor Navigation. doctorate thesis: Delft University of Technology.
Makdoom, U. (2015). 3D Indoor Routing and Visualization for the University of Redlands. Master of Science: University of Redlands.
Nagel, C. (2014). Spatio-Semantic Modelling of Indoor Environments for Indoor Navigation. Doctoral thesis: Technical University of Berlin.
OGC . (2019, Jan 13). Retrieved from http://www.opengeospatial.org/standards/citygml/
Open Geospatial Consortium. (2012-04-04). OGC City Geography Markup Language (CityGML) Encoding Standard. Version: 2.0.0. http://www.opengis.net/spec/citygml/2.0.
Ozdenizci B, Caskun V, Ok K. (2015). NFC Internal: An Indoor Navigation System. Sensors, 15(4), 7571-7595.
Petrenko, A. (2013). GENERATION OF AN INDOOR NAVIGATION NETWORK FOR THE UNIVERSITY OF SASKATCHEWAN. Master of Science: University of Saskatchewan.
Tang S J, Zhu Q, Wang W W, Zhang Y T. (2015). AUTOMATIC TOPOLOGY DERIVATION FROM IFC BUILDING MODEL FOR IN-DOOR INTELLIGENT NAVIGATION . Int Arch Photogrammetry and Remote Sensing Spatial Inf Sci, XL-4/W5, 7-11.
Tashakkori H, Rajabifard A, Kalantari M. (2015). A new 3D indoor outdoor GIS model for indoor emergency response facilitation. Building and Environment, 89, 170-182.
Teo T A, Cho K H. (2016). BIM-oriented indoor network model for indoor and outdoor combined route planning. Advanced Engineering Informatics, 30, 268–282.
Tsiliakou E, Dimopoulou E. (2016). 3D NETWORK ANALYSIS FOR INDOOR SPACE APPLICATIONS. 11th 3D Geoinfo Conference (pp. 147-154). Athens: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
Wallis, W. D. (2007). A Beginner's Guide to Graph Theory. Boston: Birkhauser.
Worboys, M. (2011). Modeling indoor space. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, 1-6.
Xu M, Hijazi I, Mebarki A, El Meouche R, Abune’meh M. (2016). Indoor guided evacuation: TIN for graph generation and crowd evacuation. GEOMATICS NATURAL HAZARDS AND RISK, 7(sup1), 47-56.
Yang L, Worboys M. (2011). Similarities and differences between outdoor and indoor space from the perspective of navigation. Poster presented at COSIT, www.worboys.org/publications/Cosit2011poster.pdf.
Yang L, Worboys M. (2015). Generation of navigation graphs for indoor space. International Journal of Geographical Information Science, 29(10), 1737-1756.
Yao Z, Nagel C, Kunde F, Hudra G, Willkomm P, Donaubauer A, Adolphi T, Kolbe T H. (2018). 3DCityDB - a 3D geodatabase solution for the management, analysis, and visualization of semantic 3D city models based on CityGML. Open Geospatial Data Software and Standards, 3:5, https://doi.org/10.1186/s40965-018-0046-7.