Navigational charts play a vital role in a ship's safety while navigating the seas, rivers or lakes. With most of the features and obstructions being out of sight -below sea-level - these charts are more critical than e.g. topographic maps. For routing but also positioning, depth information is a key aspect on these charts. This depth information is available in either depth contours, coloured depth areas and individual soundings. However with the data originating from accurate but usually erratic survey data, a visualisation of raw data is not sufficient for use in a navigational chart directly. It would not clearly convey the information to a human operator in one sight, and thus this visualisation is in need of generalisation: a simplified representation of the same data with irrelevant details being omitted. This thesis gives new insights in the generalisation process for isobaths only and proposes a new framework to deal with those. We propose a framework based on a novel auxiliary data structure to link a triangulation to the resulting isobaths: the triangle region graph. It links the position of isobaths directly to individual triangles, as well as establishes relations between the isobaths themselves. With this structure we ultimately link the survey data to the final cartographic product and thus in theory we could integrate all information across the generalisation pipeline in one and the same process. We have successfully used this framework with a basic rule-based evaluation model: we isolate conflicting isobaths, triangles and vertices based on legibility requirements and target generalisation operators on those. With this approach we can successfully maintain more of the morphology while still yielding a finely legible chart. Especially at large scale charts the results are promising: narrow channels, pits and bends remain if legibility permits. With smaller scale charts the challenge now is to generalise beyond smoothness. More radical generalisation operators are needed to omit all irrelevant details. However the overall framework using the triangle region graph as integrating mechanism has potential to do so. It is easily extensible due to its modular approach and can incorporate most depth information: from survey accuracy to size of isobaths and even golden sounding selection in the future.

We are now gradually entering the era of big data - maybe a bit too much of a buzzword, but it is not lied. Technology is evolving fast, enabling faster and more efficient data acquisition, storage, retrieval and processing. Point cloud datasets are such a type which relies on large files and lots of processing power. The rather fast evolutions in technology enable the shared idea between Delft University of Technology and Fugro of an ‘Open Point Cloud Map’. This Open Point Cloud Map aims at making point cloud datasets easily available to the public, even letting them performsimple analysis. Both Fugro and TU Delft want to take lead in development of such an environment; three student teams from TU Delft thus form a partnership with Fugro to kick-off three in-depth researches which would result in one step closer to the vision of OPCM. The ChronoCity team will focus on the time-component (Figure 1.1) in the acquainted point clouds, while the other two teams focus on location-dependency and different scales and granularities of the datasets.

The Chronocity-team strives to create an online interactive tool which gives the user the ability to view, explore and analyze massive point cloud datasets on-the-fly. Since the limited timespan in which the project should take place this would not yield a fully optimized application, but at least the general principles are defined and evaluated on for a more defined future in the development of the OPCM. A large portion of the efforts will go into making the data and analyses available to the public - in an interactive and user-friendly way; because without this availability, the underlying principles are not brought to the public also. Regarding these underlying principles the most important one is change detection. During the project a suitable algorithm is designed and evaluated for detecting new, removed and changed geometric points.