## 2 Observation Systems and their Integration
### Example Forest Fire Observation Systems  
In this section, we describe our approach for processing VGI--CONAVI (CONtextual Analysis of Volunteered Information), see also [20, 21], and we sketch a traditional forest fire information system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Nervous System** & **VGI Sensing** & **Remote Sensing** \\ \hline \multirow{4}{*}{Sensation} & Publicly available VGI is detected, filtered and organized according to the VGI virtual sensor’s specification. & Waves are detected and digitized by a satellite-mounted sensor, _i.e._ camera and series of remote sensing images are created according to the image sensor’s specifications. \\ \cline{2-3}  & Patterns are found in results, and events and situations are identified thanks to prior knowledge. & Signals with specific characteristics are detected in image series, leading to the identification of events. \\ \hline Attention & Alerting mechanisms are triggered according to context. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Event detection based on Volunteered Geographic Information (VGI) sensing compared to remote sensing (adapted from [17]). The Figure 1 (below) shows the principal phases of a complete process model to retrieve, access, analyze and disseminate user-generated geographic content. The left column corresponds to the nervous system analogy introduced in Table 2, the middle column to the equivalent high-level tasks, and the right column to the technology used. The high-level task of retrieval/sensing corresponds to the querying of Application Programming interfaces (APIs) and the subsequent storage of retrieved data. In terms of technology, the system currently uses the social media platforms Twitter and Flickr, but it could be adapted easily to use a more generic platform such as the Web 2.0 Broker [22]. During the processing and analysis phase, the module _CONAVI Analyzer_ works with the individual recording stimuli (_i.e._, Twitter texts, Flickr images with metadata). First, it validates them by checking their topicality. This check searches for forest-fire related keywords in the record, and based on keyword occurrences extracted from a manually annotated set of 6000 Tweets, assigns a topical category. The records that are likely to be about forest fires enter the next processing step and are geocoded by matching found place names with entries in gazetteers (currently based on the Geographic Information System of the European Commission). Then, the Analyzer enriches the sensations with further information (e.g., local land cover, population density, forest fire risk, distance to known fires), and from this calculates an integrated quality score that facilitates filtering for the end user and turns each recorded stimulus into a sensation with extended context information. In a next phase, the system looks for patterns among the individual sensations that have been organized according to the specification of the Analyzer Module. This is accomplished by clustering the sensations. Since forest fires are events located in space and time, we chose to cluster the sensations spatio-temporally, although other types of clustering (e.g., thematically) are possible. The spatio-temporal clustering module _CONAVI Clusterer_ aims to find groups of high-potential VGI sensations. Currently, it uses the SatScan [23] software and performs both a spatio-temporal permutation as well as Poisson-based spatio-temporal scan (corrected for population density). Any clusters found are treated as perceptions, which are again assessed for thematic accuracy. Finally, the combined (cluster) likelihood of a forest fire is calculated, and based on this information, the system can alert human experts who can decide to further investigate or act on the issue. Figure 1: Overview of approach for sensing and processing. The dissemination and alerting phase is currently implemented as a web map. In terms of technology, we use open standards, particularly the SWE suite of standards of the Open Geospatial Consortium (OGC) [4].