## 2 Observation Systems and their Integration
### Citizen-Based Geo-Sensor Networks  
For our work, the perceptions of human observers are the crucial first step in the creation of VGI. However, in contrast to a trivial interpretation of the \"citizen as sensor\" metaphor, we do not consider the individual citizen as a sensor making observations, but as an element of a larger (virtual) UGC sensor. The observed property is VGI coming from a volunteer. Thus, we focus on a higher level observation process, where a stream of VGI from multiple volunteers gets harvested under pre-defined conditions and turned into an aggregate observation. For example, if a person perceives a forest fire, takes a picture of the fire and uploads that picture together with associated keywords and information about spatial-temporal location to Flickr, we consider the upload to Flickr as a stimulus to a \"VGI sensor\", which aggregates such stimuli over some time and location windows and produces its own observations from them. The following table (Table 2) presents the central concepts of VGI sensing and event extraction, as introduced originally by [PERSON] _et al._[11] and modified in [PERSON] _et al._[17]. In particular, the table distinguishes the different entities and processing steps for VGI (middle column) and remote sensing (right column) in analogy to the (human) nervous system and reactions to external stimuli (left column). The analogy to the nervous system is particularly relevant in respect to quality assurance. Similar to the combination of multiple human senses, multiple UGC and environmental sensors may be used in combination in order to support an initial perception or to gather additional information. For \begin{table}
\begin{tabular}{c c c} \hline
**Content/Contribution** & **Explicitly volunteered** & **Implicitly volunteered** \\ \hline \multirow{2}{*}{**Explicitly geographic**} & Volunteered Geographic Information & User-generated geographic content \\  & (VGI), e.g., Open Street Map & (UGGC), e.g., place-related Tweets \\  & Volunteered information (VI), e.g., & Generic user-generated content (UGC) \\  & geo-coded Wikipedia entries & \\ \hline \end{tabular}
\end{table} 
Table 1: Typology of user-generated content. example, when a human feels something on his/her skin, this sensation is caused by the synthesis of a number of stimuli (e.g., nerve cells reporting something). If there are patterns in these sensations, such as a repeated light sensation moving in a direction, this might lead to the perception of having an insect on one's skin. Depending on the type of perception and situation, this could be dangerous or irrelevant. In the first case, the human directs his/her attention to the origin of the first (tactile) perception and reacts depending on a second (visual) perception, e.g., whether s/he sees a fly or wasp. Similarly, VGI sensing might detect a high number of tweets and images about forest fires in a given area. Combining this first perception with perceptions from other sources such as remote sensing improves the information basis on which to react. In order to exploit the full potential of sensed VGI, it needs integration with existing spatial data infrastructures, either through tight coupling in common data structures, or through loose coupling in common visualizations. Similarly to traditional remote or in-situ sensors, UGC sensing faces the problem of multiple sensor types and protocols defined by their manufacturers. To publish and integrate this sensor data with spatial data infrastructures, any captured raw data needs to be transformed to standardized protocols of the infrastructures. Traditionally, this has been done by manually implementing adapters for each sensor type, resulting in extensive efforts when developing large-scale systems [18]. More recent work, like the Sensor Interface Descriptor (SID) [19], enables the declarative description of sensor interfaces, including the definition of the communication protocol and processing steps.