Abstract
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.
Highlights
The use of 3D city models is becoming increasingly important, as can be seen in various development of Spatial Data Infrastructures (SDIs) [1]
The lowest compression rate was found to be for Fast Info technique, while LZMA produced better results in comparison with other techniques used alone, which justifies its integration in our CityGML Schema Aware Compressor (CitySAC) encoding system
We presented CitySAC encoding system which efficiently compresses data models with large geometries
Summary
The use of 3D city models is becoming increasingly important, as can be seen in various development of Spatial Data Infrastructures (SDIs) [1]. Transmitting models in CityGML are considered impractical, due to computational costs involved in storage and transfer of schema over distributed networks This is, for example, problematic when a city model is required for visualization purposes over a distributed environment. This paper presents an approach, called CitySAC (CityGML Schema Aware Compressor), which compresses CityGML and other data models constituting large geometries. It introduces a novel technique for encoding CityGML, which conforms to UTF-8 standards and embeds dictionary compression, compartmentalization and point compression for input geometries. The proposed solution contributes to the development of XML compression technique that enables an efficient handling of spatial data models within CityGML framework. White Space Element Attribute Attribute Value Value Element Close URI / Face-set Pair
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