Visibility computation through image generalization

This dissertation introduces the image generalization paradigm for computing visibility. The paradigm is based on the observation that an image is a powerful tool for computing visibility. An image can be rendered efficiently with the support of graphics hardware and each of the millions of pixels in the image reports a visible geometric primitive. However, the visibility solution computed by a conventional image is far from complete. A conventional image has a uniform sampling rate which can miss visible geometric primitives with a small screen footprint. A conventional image can only find geometric primitives to which there is direct line of sight from the center of projection (i.e. the eye) of the image; therefore, a conventional image cannot compute the set of geometric primitives that become visible as the viewpoint translates, or as time changes in a dynamic dataset. Finally, like any sample-based representation, a conventional image can only confirm that a geometric primitive is visible, but it cannot confirm that a geometric primitive is hidden, as that would require an infinite number of samples to confirm that the primitive is hidden at all of its points. ^ The image generalization paradigm overcomes the visibility computation limitations of conventional images. The paradigm has three elements. (1) Sampling pattern generalization entails adding sampling locations to the image plane where needed to find visible geometric primitives with a small footprint. (2) Visibility sample generalization entails replacing the conventional scalar visibility sample with a higher dimensional sample that records all geometric primitives visible at a sampling location as the viewpoint translates or as time changes in a dynamic dataset; the higher-dimensional visibility sample is computed exactly, by solving visibility event equations, and not through sampling. Another form of visibility sample generalization is to enhance a sample with its trajectory as the geometric primitive it samples moves in a dynamic dataset. (3) Ray geometry generalization redefines a camera ray as the set of 3D points that project at a given image location; this generalization supports rays that are not straight lines, and enables designing cameras with non-linear rays that circumvent occluders to gather samples not visible from a reference viewpoint. ^ The image generalization paradigm has been used to develop visibility algorithms for a variety of datasets, of visibility parameter domains, and of performance-accuracy tradeoff requirements. These include an aggressive from-point visibility algorithm that guarantees finding all geometric primitives with a visible fragment, no matter how small primitive\u27s image footprint, an efficient and robust exact from-point visibility algorithm that iterates between a sample-based and a continuous visibility analysis of the image plane to quickly converge to the exact solution, a from-rectangle visibility algorithm that uses 2D visibility samples to compute a visible set that is exact under viewpoint translation, a flexible pinhole camera that enables local modulations of the sampling rate over the image plane according to an input importance map, an animated depth image that not only stores color and depth per pixel but also a compact representation of pixel sample trajectories, and a curved ray camera that integrates seamlessly multiple viewpoints into a multiperspective image without the viewpoint transition distortion artifacts of prior art methods

Multiscale visualization approaches for Volunteered Geographic Information and Location-based Social Media

Today, “zoomable” maps are a state-of-the-art way to explore the world, available to anyone with Internet access. However, the process of creating this visualization has been rather loosely investigated and documented. Nevertheless, with an increasing amount of available data, interactive maps have become a more integral approach to visualizing and exploring big datasets and user-generated data. OpenStreetMap and online platforms such as Twitter and Flickr offer application programming interfaces (APIs) with geographic information. They are well-known examples of this visualization challenge and are often used as examples. In addition, an increasing number of public administrations collect open data and publish their data sets, which makes the task of visualization even more relevant. This dissertation deals with the visualization of user-generated geodata as a multiscale map. The basics of today’s multiscale maps—their history, technologies, and possibilities—are explored and abstracted. This work introduces two new multiscale-focused visualization approaches for point data from volunteered geographic information (VGI) and location-based social media (LBSM). One contribution of this effort is a visualization methodology for spatially referenced information in the form of point geometries, using nominally scaled data from social media such as Twitter or Flickr. Typical for this data is a high number of social media posts in different categories—a post on social media corresponds to a point in a specific category. Due to the sheer quantity and similar characteristics, the posts appear generic rather than unique. This type of dataset can be explored using the new method of micro diagrams to visualize the dataset on multiple scales and resolutions. The data is aggregated into small grid cells, and the numerical proportion is shown with small diagrams, which can visually merge into heterogenous areas through colors depicting a specific category. The diagram sizes allow the user to estimate the overall number of aggregated points in a grid cell. A different visualization approach is proposed for more unique points, considered points of interest (POI), based on the selection method. The goal is to identify more locally relevant points from the data set, considered more important compared to other points in the neighborhood, which are then compared by numerical attribute. The method, derived from topographic isolation and called discrete isolation, is the distance from one point to the next with a higher attribute value. By using this measure, the most essential points can be easily selected by choosing a minimum distance and producing a homogenous spatial of the selected points within the chosen dataset. The two newly developed approaches are applied to multiscale mapping by constructing example workflows that produce multiscale maps. The publicly available multiscale mapping workflows OpenMapTiles and OpenStreetMap Carto, using OpenStreetMap data, are systematically explored and analyzed. The result is a general workflow for multiscale map production and a short overview of the toolchain software. In particular, the generalization approaches in the example projects are discussed and these are classified into cartographic theories on the basis of literature. The workflow is demonstrated by building a raster tile service for the micro diagrams and a vector tile service for the discrete isolation, able to be used with just a web browser. In conclusion, these new approaches for point data using VGI and LBSM allow better qualitative visualization of geodata. While analyzing vast global datasets is challenging, exploring and analyzing hidden data patterns is fruitful. Creating this degree of visualization and producing maps on multiple scales is a complicated task. The workflows and tools provided in this thesis will make map production on a worldwide scale easier.:1 Introduction 1 1.1 Motivation .................................................................................................. 3 1.2 Visualization of crowdsourced geodata on multiple scales ............ 5 1.2.1 Research objective 1: Visualization of point collections ......... 6 1.2.2 Research objective 2: Visualization of points of interest ......... 7 1.2.3 Research objective 3: Production of multiscale maps ............. 7 1.3 Reader’s guide ......................................................................................... 9 1.3.1 Structure ........................................................................................... 9 1.3.2 Related Publications ....................................................................... 9 1.3.3 Formatting and layout ................................................................. 10 1.3.4 Online examples ........................................................................... 10 2 Foundations of crowdsourced mapping on multiple scales 11 2.1 Types and properties of crowdsourced data .................................. 11 2.2 Currents trends in cartography ......................................................... 11 2.3 Definitions .............................................................................................. 12 2.3.1 VGI .................................................................................................. 12 2.3.2 LBSM .............................................................................................. 13 2.3.3 Space, place, and location......................................................... 13 2.4 Visualization approaches for crowdsourced geodata ................... 14 2.4.1 Review of publications and visualization approaches ........... 14 2.4.2 Conclusions from the review ...................................................... 15 2.4.3 Challenges mapping crowdsourced data ................................ 17 2.5 Technologies for serving multiscale maps ...................................... 17 2.5.1 Research about multiscale maps .............................................. 17 2.5.2 Web Mercator projection ............................................................ 18 2.5.3 Tiles and zoom levels .................................................................. 19 2.5.4 Raster tiles ..................................................................................... 21 2.5.5 Vector tiles .................................................................................... 23 2.5.6 Tiling as a principle ..................................................................... 25 3 Point collection visualization with categorized attributes 26 3.1 Target users and possible tasks ....................................................... 26 3.2 Example data ......................................................................................... 27 3.3 Visualization approaches .................................................................... 28 3.3.1 Common techniques .................................................................... 28 3.3.2 The micro diagram approach .................................................... 30 3.4 The micro diagram and its parameters ............................................ 33 3.4.1 Aggregating points into a regular structure ............................ 33 3.4.2 Visualizing the number of data points ...................................... 35 3.4.3 Grid and micro diagrams ............................................................ 36 3.4.4 Visualizing numerical proportions with diagrams .................. 37 3.4.5 Influence of color and color brightness ................................... 38 3.4.6 Interaction options with micro diagrams .................................. 39 3.5 Application and user-based evaluation ............................................ 39 3.5.1 Micro diagrams in a multiscale environment ........................... 39 3.5.2 The micro diagram user study ................................................... 41 3.5.3 Point collection visualization discussion .................................. 47 4 Selection of POIs for visualization 50 4.1 Approaches for point selection .......................................................... 50 4.2 Methods for point selection ................................................................ 51 4.2.1 Label grid approach .................................................................... 52 4.2.2 Functional importance approach .............................................. 53 4.2.3 Discrete isolation approach ....................................................... 54 4.3 Functional evaluation of selection methods .................................... 56 4.3.1 Runtime comparison .................................................................... 56 4.3.2 Use cases for discrete isolation ................................................ 57 4.4 Discussion of the selection approaches .......................................... 61 4.4.1 A critical view of the use cases ................................................. 61 4.4.2 Comparing the approaches ........................................................ 62 4.4.3 Conclusion ..................................................................................... 64 5 Creating multiscale maps 65 5.1 Examples of multiscale map production .......................................... 65 5.1.1 OpenStreetMap Infrastructure ................................................... 66 5.1.2 OpenStreetMap Carto ................................................................. 67 5.1.3 OpenMapTiles ............................................................................... 73 5.2 Methods of multiscale map production ............................................ 80 5.2.1 OpenStreetMap tools ................................................................... 80 5.2.2 Geoprocessing .............................................................................. 80 5.2.3 Database ........................................................................................ 80 5.2.4 Creating tiles ................................................................................. 82 5.2.5 Caching .......................................................................................... 82 5.2.6 Styling tiles .................................................................................... 82 5.2.7 Viewing tiles ................................................................................... 83 5.2.8 The stackless approach to tile creation ................................... 83 5.3 Example workflows for creating multiscale maps ........................... 84 5.3.1 Raster tiles: OGC services and micro diagrams .................... 84 5.3.2 Vector tiles: Slippy map and vector tiles ................................. 87 5.4 Discussion of approaches and workflows ....................................... 90 5.4.1 Map production as a rendering pipeline .................................. 90 5.4.2 Comparison of OpenStreetMap Carto and OpenMapTiles .. 92 5.4.3 Discussion of the implementations ........................................... 93 5.4.4 Generalization in map production workflows .......................... 95 5.4.5 Conclusions ................................................................................. 101 6 Discussion 103 6.1 Development for web mapping ........................................................ 103 6.1.1 The role of standards in map production .............................. 103 6.1.2 Technological development ..................................................... 103 6.2 New data, new mapping techniques? ............................................. 104 7 Conclusion 106 7.1 Visualization of point collections ..................................................... 106 7.2 Visualization of points of interest ................................................... 107 7.3 Production of multiscale maps ........................................................ 107 7.4 Synthesis of the research questions .............................................. 108 7.5 Contributions ....................................................................................... 109 7.6 Limitations ............................................................................................ 110 7.7 Outlook ................................................................................................. 111 8 References 113 9 Appendix 130 9.1 Zoom levels and Scale ...................................................................... 130 9.3 Full information about selected UGC papers ................................ 131 9.4 Timeline of mapping technologies .................................................. 133 9.5 Timeline of map providers ................................................................ 133 9.6 Code snippets from own map production workflows .................. 134 9.6.1 Vector tiles workflow ................................................................. 134 9.6.2 Raster tiles workflow.................................................................. 137Heute sind zoombare Karten Alltag für jeden Internetznutzer. Die Erstellung interaktiv zoombarer Karten ist allerdings wenig erforscht, was einen deutlichen Gegensatz zu ihrer aktuellen Bedeutung und Nutzungshäufigkeit darstellt. Die Forschung in diesem Bereich ist also umso notwendiger. Steigende Datenmengen und größere Regionen, die von Karten abgedeckt werden sollen, unterstreichen den Forschungsbedarf umso mehr. Beispiele für stetig wachsende Datenmengen sind Geodatenquellen wie OpenStreetMap aber auch freie amtliche Geodatensätze (OpenData), aber auch die zunehmende Zahl georeferenzierter Inhalte auf Internetplatformen wie Twitter oder Flickr zu nennen. Das Thema dieser Arbeit ist die Visualisierung eben dieser nutzergenerierten Geodaten mittels zoombarer Karten. Dafür wird die Entwicklung der zugrundeliegenden Technologien über die letzten zwei Jahr-zehnte und die damit verbundene Möglichkeiten vorgestellt. Weitere Beiträge sind zwei neue Visualisierungsmethoden, die sich besonders für die Darstellung von Punktdaten aus raumbezogenen nutzergenerierten Daten und georeferenzierte Daten aus Sozialen Netzwerken eignen. Ein Beitrag dieser Arbeit ist eine neue Visualisierungsmethode für raumbezogene Informationen in Form von Punktgeometrien mit nominal skalierten Daten aus Sozialen Medien, wie beispielsweise Twitter oder Flickr. Typisch für diese Daten ist eine hohe Anzahl von Beiträgen mit unterschiedlichen Kategorien. Wobei die Beiträge, bedingt durch ihre schiere Menge und ähnlicher Ei-genschaften, eher generisch als einzigartig sind. Ein Beitrag in den So-zia len Medien entspricht dabei einem Punkt mit einer bestimmten Katego-rie. Ein solcher Datensatz kann mit der neuen Methode der „micro diagrams“ in verschiedenen Maßstäben und Auflösungen visualisiert und analysiert werden. Dazu werden die Daten in kleine Gitterzellen aggregiert. Die Menge und Verteilung der über die Kategorien aggregierten Punkte wird durch kleine Diagramme dargestellt, wobei die Farben die verschiedenen Kategorien visualisieren. Durch die geringere Größe der einzelnen Diagramme verschmelzen die kleinen Diagramme visuell, je nach der Verteilung der Farben für die Kategorien. Bei genauerem Hinsehen ist die Schätzung der Menge der aggregierten Punkte über die Größe der Diagramme die Menge und die Verteilung über die Kategorien möglich. Für einzigartigere Punkte, die als Points of Interest (POI) angesehen werden, wird ein anderer Visualisierungsansatz vorgeschlagen, der auf einer Auswahlmethode basiert. Ziel ist es dabei lokal relevantere Punkte aus dem Datensatz zu identifizieren, die im Vergleich zu anderen Punkten in der Nachbarschaft des Punktes verglichen nach einem numerischen Attribut wichtiger sind. Die Methode ist von dem geographischen Prinzip der Dominanz von Bergen abgeleitet und wird „discrete isolation“ genannt. Es handelt sich dabei um die Distanz von einem Punkt zum nächsten mit einem höheren Attributwert. Durch die Verwendung dieses Maßes können lokal bedeutende Punkte leicht ausgewählt werden, indem ein minimaler Abstand gewählt und so räumlich gleichmäßig verteilte Punkte aus dem Datensatz ausgewählt werden. Die beiden neu vorgestellten Methoden werden in den Kontext der zoombaren Karten gestellt, indem exemplarische Arbeitsabläufe erstellt werden, die als Er-gebnis eine zoombare Karte liefern. Dazu werden die frei verfügbaren Beispiele zur Herstellung von weltweiten zoombaren Karten mit nutzergenerierten Geo-daten von OpenStreetMap, anhand der Kartenprojekte OpenMapTiles und O-penStreetMap Carto analysiert und in Arbeitsschritte gegliedert. Das Ergebnis ist ein wiederverwendbarer Arbeitsablauf zur Herstellung zoombarer Karten, ergänzt durch eine Auswahl von passender Software für die einzelnen Arbeits-schritte. Dabei wird insbesondere auf die Generalisierungsansätze in den Beispielprojekten eingegangen und diese anhand von Literatur in die kartographische Theorie eingeordnet. Zur Demonstration des Workflows wird je ein Raster Tiles Dienst für die „micro diagrams“ und ein Vektor Tiles Dienst für die „discrete isolation“ erstellt. Beide Dienste lassen sich mit einem aktuellen Webbrowser nutzen. Zusammenfassend ermöglichen diese neuen Visualisierungsansätze für Punkt-daten aus VGI und LBSM eine bessere qualitative Visualisierung der neuen Geodaten. Die Analyse riesiger globaler Datensätze ist immer noch eine Herausforderung, aber die Erforschung und Analyse verborgener Muster in den Daten ist lohnend. Die Erstellung solcher Visualisierungen und die Produktion von Karten in verschiedenen Maßstäben ist eine komplexe Aufgabe. Die in dieser Arbeit vorgestellten Arbeitsabläufe und Werkzeuge erleichtern die Erstellung von Karten in globalem Maßstab.:1 Introduction 1 1.1 Motivation .................................................................................................. 3 1.2 Visualization of crowdsourced geodata on multiple scales ............ 5 1.2.1 Research objective 1: Visualization of point collections ......... 6 1.2.2 Research objective 2: Visualization of points of interest ......... 7 1.2.3 Research objective 3: Production of multiscale maps ............. 7 1.3 Reader’s guide ......................................................................................... 9 1.3.1 Structure ........................................................................................... 9 1.3.2 Related Publications ....................................................................... 9 1.3.3 Formatting and layout ................................................................. 10 1.3.4 Online examples ........................................................................... 10 2 Foundations of crowdsourced mapping on multiple scales 11 2.1 Types and properties of crowdsourced data .................................. 11 2.2 Currents trends in cartography ......................................................... 11 2.3 Definitions .............................................................................................. 12 2.3.1 VGI .................................................................................................. 12 2.3.2 LBSM .............................................................................................. 13 2.3.3 Space, place, and location......................................................... 13 2.4 Visualization approaches for crowdsourced geodata ................... 14 2.4.1 Review of publications and visualization approaches ........... 14 2.4.2 Conclusions from the review ...................................................... 15 2.4.3 Challenges mapping crowdsourced data ................................ 17 2.5 Technologies for serving multiscale maps ...................................... 17 2.5.1 Research about multiscale maps .............................................. 17 2.5.2 Web Mercator projection ............................................................ 18 2.5.3 Tiles and zoom levels .................................................................. 19 2.5.4 Raster tiles ..................................................................................... 21 2.5.5 Vector tiles .................................................................................... 23 2.5.6 Tiling as a principle ..................................................................... 25 3 Point collection visualization with categorized attributes 26 3.1 Target users and possible tasks ....................................................... 26 3.2 Example data ......................................................................................... 27 3.3 Visualization approaches .................................................................... 28 3.3.1 Common techniques .................................................................... 28 3.3.2 The micro diagram approach .................................................... 30 3.4 The micro diagram and its parameters ............................................ 33 3.4.1 Aggregating points into a regular structure ............................ 33 3.4.2 Visualizing the number of data points ...................................... 35 3.4.3 Grid and micro diagrams ............................................................ 36 3.4.4 Visualizing numerical proportions with diagrams .................. 37 3.4.5 Influence of color and color brightness ................................... 38 3.4.6 Interaction options with micro diagrams .................................. 39 3.5 Application and user-based evaluation ............................................ 39 3.5.1 Micro diagrams in a multiscale environment ........................... 39 3.5.2 The micro diagram user study ................................................... 41 3.5.3 Point collection vis

Visualizing and Interacting with Geospatial Networks:A Survey and Design Space

This paper surveys visualization and interaction techniques for geospatial networks from a total of 95 papers. Geospatial networks are graphs where nodes and links can be associated with geographic locations. Examples can include social networks, trade and migration, as well as traffic and transport networks. Visualizing geospatial networks poses numerous challenges around the integration of both network and geographical information as well as additional information such as node and link attributes, time, and uncertainty. Our overview analyzes existing techniques along four dimensions: i) the representation of geographical information, ii) the representation of network information, iii) the visual integration of both, and iv) the use of interaction. These four dimensions allow us to discuss techniques with respect to the trade-offs they make between showing information across all these dimensions and how they solve the problem of showing as much information as necessary while maintaining readability of the visualization. https://geonetworks.github.io.Comment: To be published in the Computer Graphics Forum (CGF) journa

Contact and HiL interaction in multibody based machinery simulators

[Abstract] Multibody simulators allow to predict and evaluate the motion of machines and mechanisms under the action of the user and the interaction with the simulated environment. Interactive simulators guided by a human or a piece of hardware must be efficient enough to compute the state of the system in real time. ?erefore, employing fast and sufficiently accurate techniques is a must. In this work, generic tools for the implementation of this kind of simulators are provided. Efficient multibody formulations are reviewed for implementing real-time simulators. ?e index-3 Augmented Lagrange formulation with projections of velocities and accelerations is selected, due to its efficiency and stability. ?e integration of the equations of motion follows the Generalized-a method, which provides high-frequency dissipation, and can be unconditionally stable and secondorder accurate if suitable integrator parameters are chosen. Contact modeling and detection is essential for computing the interaction among the mechanisms and the simulated environment. Normal and tangential contact force models are presented. For the normal contact, a Hertz-type Hunt- Crossley model is chosen. ?e tangential force model is based on Coulomb’s law, and includes stiction and viscous friction effects. Both models were compared with the output of the Bowden-Leben stick-slip experiment. A real-time, simplified terrain model featuring digging forces for excavator simulators is also discussed. Several techniques are shown for detecting colliding bodies at run-time. ?e collision detection process is divided into two stages. ?e first one is a broad range and coarse grained process, where potentially colliding pairs of objects are discovered. Spatial and hierarchical division techniques as Octrees, BSP-trees and Directed Acyclic Graphs are presented for this purpose. In the second stage, fine-detailed contact properties are computed from each pair of bodies. Several models are presented for testing object enclosing volumes or more complex surfaces discretized as triangular meshes. State-of-the-art, Commercial Off ?e Shelf hardware devices are presented as the physical foundation of a simulator. Industrial-quality controllers, projection screens and audio devices are reviewed for this purpose. ?e implementation details for the use of those devices are also considered. Network communication procedures between the simulator and monitoring nodes are discussed, too. Finally, a particular implementation of all the techniques described in previous chapters is presented in the form of an interactive excavator simulator, which features all the degrees of freedom of the machine, and is able to perform earthmoving operations in a realistic environment. Monitoring capabilities are also available, and any training session can be defined by user scripts. ?e techniques described in this document constitute a generic and efficient compendium of algorithms that are well-fi?ed for medium or low-end computational systems, as desktop or even laptop computers