Next Article in Journal
Achieving Neuroplasticity in Artificial Neural Networks through Smart Cities
Previous Article in Journal
The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City
Open AccessArticle

CitySAC: A Query-Able CityGML Compression System

1
Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia
2
Geomatics Division, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, 08860 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Current Address: Geomatics Division, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, Barcelona, Spain.
Smart Cities 2019, 2(1), 106-117; https://doi.org/10.3390/smartcities2010008
Received: 2 January 2019 / Revised: 11 February 2019 / Accepted: 14 March 2019 / Published: 19 March 2019
Spatial Data Infrastructures (SDIs) are frequently used to exchange 2D & 3D data, in areas such as city planning, disaster management, urban navigation and many more. City Geography Mark-up Language (CityGML), an Open Geospatial Consortium (OGC) standard has been developed for the storage and exchange of 3D city models. Due to its encoding in XML based format, the data transfer efficiency is reduced which leads to data storage issues. The use of CityGML for analysis purposes is limited due to its inefficiency in terms of file size and bandwidth consumption. This paper introduces XML based compression technique and elaborates how data efficiency can be achieved with the use of schema-aware encoder. We particularly present CityGML Schema Aware Compressor (CitySAC), which is a compression approach for CityGML data transaction within SDI framework. Our test results show that the encoding system produces smaller file size in comparison with existing state-of-the-art compression methods. The encoding process significantly reduces the file size up to 7–10% of the original data. View Full-Text
Keywords: Spatial Data Infrastructure; 3D data; CityGML; encoding; Urban Applications Spatial Data Infrastructure; 3D data; CityGML; encoding; Urban Applications
Show Figures

Figure 1

MDPI and ACS Style

Siew, C.B.; Kumar, P. CitySAC: A Query-Able CityGML Compression System. Smart Cities 2019, 2, 106-117.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Back to TopTop