COLABIS will design and implement a Web platform that enables and eases the development of urban early warning systems, specifically focusing on information fusion derived from sensors, crowdsourcing, geosimulations, as well as administrative and historical data. The project focusses on local heavy rain, flooding and cascading events affecting urban water and sewage infrastructures but also traffic infrastructures.

To support decision makers with timely and value added information, input data from various sources need to be combined. These data fusion processes comprise data retrieval, enhancement, harmonization, similarity measures, matching, conflict detection and resolving. The consistency and reliability of results essentially relies on the underlying data quality, which varies over the different data sources. Therefore, quality metrics will play a major role in COLABIS.

The department Spatial Information Management will focus on two aspects. One aspect is the development of robust and interoperable methods based on Linked Data mechanisms to implement web-based functionality for spatio-temporal fusion and evaluation. The scientific focus is on collaborative data transformation, semantic annotation of dynamic geodata sets and the generation and exploitation of linked geodata.

A second aspect is the classification and integration of building surfaces into 3D city models to support examination of wash-off processes. A feasibility study on the applicability of cadastre data and virtual 3D city models as data sources to estimate the distribution of respective pollutants sources will be conducted, to estimate substance disposition. It will be analysed whether this information could serve as a proxy for substance deposition and whether it could give an indication for vertical wash-off effects.