Q\&A fora have developed into a precious tool for online knowledge exchange. Being community-driven, openly accessible and free, getting an answer to a complicated question can nowadays be a matter of minutes. This research investigates the specific case of gis.stackexchange, a technical Q&A forum used by Geographic Information Systems professionals. A central topic is the interaction between users. Based on existing research, an automated approach is elaborated and a big data analysis is performed. Also, a link between the outcomes and self-reflective behaviour found on the community meta forum is established. Factors positively influencing interaction as defined by the approach are identified (implication of the author(s), presence of images and code snippets). Furthermore, weaknesses such as the low share of interactions associated to an alteration (of the original post) are also discussed. These outcomes eventually lead to a series of recommendations for the forum itself and related formats.

Recently, the application of machine learning and data fusion techniques on hyperspectral imagery have demonstrated potential for ground cover classification at material level. Hereby, specific locations of resources enclosed in cities (e.g. roof materials) can be identified, which is critically relevant within the field of urban mining. A limitation of this approach is the so-called 'pepper and salt effect', the oversensitivity of the classifiers to spectral variations within a pixel (e.g. chimneys, roof windows). Identifying and correcting affected pixels can be done statistically (e.g. using a majority filter), but not in cases where spectral variations affect a majority of pixels characterizing a surface. A solution to this limitation would be the usage of 3D city models containing the objects inducing the spectral variations. However, such highly detailed 3D city models are often unavailable as they cannot be produced automatically yet. An alternative covered by this research is to use a less detailed 3D city model and semantically enrich it with the required data. As 3D city models are usually produced using a point cloud, such a point cloud is used to perform the enrichment. The main research question addressed is therefore: How can a CityGML LOD2 model be semantically enriched in order to improve material classification performed on roof surfaces?.      To address this, an existing LOD2 model was compared to a point cloud acquired by Ligth Detecation and Ranging and 'deviation' points were identified. This identification uses a distance check for seed selection and performs a region growing with an orientation check. In a subsequent step, 'deviation' point regions were translated into a geometric shape by usage of their Voronoi diagram and fused with the pixels of hyperspectral imagery. Part of this research is also a nominal validation analyzing a total of 41 buildings and 831 pixels located in the south of Rotterdam (Netherlands). Overall kappa values of up to 0.7 and commission errors as low as 10% (for the class 'clean' pixels) were obtained, showing potential of the chosen method. Additionally, a rational validation was performed to assess the impact of potential tolerance of classifiers for 'spectral deviations'. This one only included 10 buildings, but took into account 328 pixels located up to 30% outside the roof surface A main outcome is the recommendation on settings to use depending on the specific user needs. To accurately quantify materials, relatively 'loose' settings are recommended. In contrast, to identify presence of materials, stricter settings are recommended. Beyond this, recommendations to data suppliers and potential applications of the method to other fields are formulated.

Vario-scale is a new mapping technique which automatically generalizes maps from a baselayer of faces. Applications of vario-scale are continuous, smooth zoom in web maps,multi-scale representation in one map and being able to generate maps at arbitrary scale. Also,this would only require having to maintain the dataset at the highest scale level, since all otherscales are derived from it.Potentially, vario-scale could be an alternative for current web maps and generalizationalgorithms. The Dutch national mapping agency, Kadaster, currently employs its owngeneralization process. However, they would like to know whether the users of theirtopographic datasets are interested in vario-scale. At this moment, there is a workingimplementation of vario scale (made by dr. ir. Martijn Meijers). This implementation,however, is still lacking in, for example, cartographic quality. Therefore the research questionin this project is: how can the implementation of vario-scale be improved to better meet theneeds for end users of Kadaster topographic data?This question is answered by questioning surveying users of Kadaster data on what theywould like to see improved about the existing implementation. Combining this with anexploration of the current software leads to an attempt at improving the currentimplementation. The project goal is set as enabling the road network visualization and mobilemap adaptation. Road network visualization is achieved by building the roads space scalecube and overlay with the background area at the front-end. Mobile map adaptation is realizedby creating the touch screen interaction between the device and the user. Finally, a validationsurvey is conducted to examine the difference between the original vario scaleimplementation and the adapted one.