The surface water extent around the world is constantly changing due to natural factors (e.g. geology and over-abstraction of water), climate change (e.g. higher water evaporation due to warmer climate) or human activities (e.g. reservoir construction). Water reservoirs are important for the management of the ecosystem, as both humans and the natural environment depend highly on them for their existence and well being. Flood control, agricultural irrigation, electricity generation, drinking and municipal water supply are only some of their main uses. Considering this, it is of high importance to have accurate maps that depict the reservoir outlines to determine their surface extend and storage capacity. However, their extend is not always well defined or there are discrepancies between various surface water datasets. This dissertation aims to provide an answer about which datasets match better as well as identifying the problematic areas by performing a quality control analysis. The main challenge of this thesis is that all available datasets have certain limitations regarding their coverage and quality. The waterbody delineation from satellite images is affected by the atmospheric conditions (e.g cloud obstructions) or topographic elements that create artifacts and influence the correct classification of water pixels. OpenStreetmap (OSM) on the other hand, has uncertain quality over locations, as the data is freely supplied by volunteers. Moreover, HydroLAKES which was created based, amongst others, on the Global Reservoir and Dam Dataset (GRanD), is still incomplete. In this thesis, an intercomparison of accuracy algorithm that can perform large scale analysis is created, by using the country of Angola as a use case, five datasets as input (Global Surface Water, Sentinel 2, OSM, HydroLAKES, GRaND) in both raster and vector format and the cloud processing platform of Google Earth engine. The identification of similarities or mismatches between the datasets is performed in terms of positional accuracy. Two quality measures have been considered for the pairwise comparison of features: percentage of overlap and Hausdorff distance. In addition the completeness of the datasets respectively to the total common water area of the water reservoir datasets is reviewed. The results of this research shows that large scale analysis for the comparison of accuracy between water reservoir datasets of different formats is possible. The pre-processing of the input Satellite data is semi-automated. The created automated algorithm for the main analysis offers information for all corresponding features between datasets. More specifically, statistics about the shape similarity, the percentage of overlap and the water area completeness of the datasets are being presented.

With the rapid growth in point cloud acquisition technologies the recent years we have the ability to measure large quantities of 3D points of significantly detailed and geometrically composite scenes such as urban environments. This advantage can be exploited and used for direct analysis on point clouds. A direct point cloud analysis has several advantages over for example 3D surface reconstruction, such as the end result having more details and the computation being less expensive. In order to make a point cloud representation a suitable alternative for other types of 3D city models, they need to be semantically enriched, resulting in a rich point cloud. One element of this enrichment is the detection of objects, such as windows. Extracting these from facades is specifically what this research revolves around, which can be done by taking advantage of the fact that they show up as holes, since lasers of the point cloud scanner do not properly reflect on them. Two different general approaches are taken to detect windows in a by mobile laser scanner obtained point cloud of Noordereiland, Rotterdam, The Netherlands.