## 1 Introduction  
The integration of sensor-based information into analysis and decision making has been a research topic for many years [1; 2; 3]. Standards such as the Sensor Web Enablement (SWE) suite of specifications, which is currently under major revision [4], help to establish syntactical and structural interoperability. This includes possibilities for integrating information produced by physical sensors and by environmental simulations [5]. Several approaches addressing semantic interoperability have been proposed. Most notably, the W3C recently released a sophisticated ontology of observations and measurements [6], and a lightweight approach for the semantic enablement of the Sensor Web has been suggested [3]. Before that, the ontology of observation had been hindered by, among other factors, the naive idea of a measurement instrument being an objective reporter of the mind-independent state of the world. This commonly held view neglects the fact that instruments are built and calibrated by human beings. Neither the choice of the observed entity, nor the quality assigned to it, nor its link to a stimulus providing the information, nor the value assignment to the result, are mind-independent processes. All of them involve human conceptualizations, though these are more amenable to grounding and agreement than anything else in the generation of information. Thus, an understanding of observations amenable to semantic modeling has been achieved and standardized, but mature applications that illustrate the use of such work for geospatial information integration are still missing. In parallel, we have witnessed a surge of geographic information provided by the public to the public via the Internet. Resembling virtual sensors, citizens provide this so-called Volunteered Geographic Information (VGI) [7] through Web 2.0 services by posting images or videos (Flickr, Panoramio, YouTube, Facebook), blogging or micro-blogging (Twitter, Facebook), surveying and updating geographic information (OpenStreetMap), posting reviews and opinions (TripAdvisor, Foodspotting, GoogleLocal) or playing games (greenGoose). Considering the increase in mobile Internet access through smartphones and the number of (geo-)social media platforms, one can expect the amount of VGI to grow continually in the near future. This new abundance of VGI has several advantages over the traditional authoritative gathering, maintaining and disseminating of geographic information. First, it is more up-to-date, because a larger number of \"surveyors\" reports new information or changes to existing information in near real-time. Second, VGI can be very rich in content, providing pre-processed information instead of raw data. As information portals such as EyeOnEarth [8] or GEO-Wiki [9] illustrate, the provided information often complements the data coming from traditional sensor networks. However, there are several challenges associated with VGI. The technology-driven development leads to frequent changes in data retrieval and data structures, since new platforms emerge, old ones disappear, and prevailing ones modify their user and programming interfaces. Further, most VGI is poorly structured and has little meta-data, so that quality control proves difficult. Even comparatively well-structured and quality-controlled platforms such as OpenStreetMap have to deal with these issues. Thus, the integration of VGI with existing sensor networks and spatial data infrastructures is a challenging task [10]. The increased diversity of information channels and provided messages makes it even more difficult to establish systems for information combination. However, if one succeeds in joining data from various channels, integrated analyses, such as the identification of co-occurring observation results, would improve the richness and quality of the derived information. For example,flooding events that remained unnoticed by classical earth observing systems, could already be detected based on user contributed content on the web [11].