Integration of CityGML and Air Quality Spatio-Temporal Data Series via OGC SOS

Cities have always been considered a horsepower of industrial growth over centuries. However, urbanization often has environmental effects. In this paper we will focus on one of them, namely air pollution, and how we can integrate 3D city models and air quality sensor data. In most cases, air quality data is acquired from static sensor systems like weather stations, which lack the coverage required for urban simulation. Applying mobile data for air quality models is more reasonable due to the spatial coverage of the urban model. Yet academic research has been scarce on the integration of city models and spatio-temporal varying sensor data. To close the gap, this paper proposes a novel approach to integrate city model data and spatio-temporal data in an interoperable and standardized way. The geometry aspects of a city are described by those defined in CityGML and the air spatio-temporal varying data is retrieved by OGC Sensor Observation Service. The approach uses Cesium JS, a JavaScript library for visualizing the semantical city model stored in CityGML and the OGC SOS observation service from 52°North for the air quality data transmission. Keywords—CityGML; SOS; Spatio-Temporal; Varying Data; Air Quality