Research Collection Master Thesis Automatic generation of tourist maps Author(s): Grabler, Floraine Publication Date: 2008 Permanent Link: https://doi.org/10.3929/ethz-a-005632669 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library Automatic Generation of Tourist Maps Floraine Grabler Master Thesis Advisors: M. Agrawala, B. Sumner and M. Pauly Department of Computer Science UC Berkeley, ETH Zurich April 1, 2008 2 Abstract Tourist maps are essential resources for visitors to an unfamiliar city because they vi- sually highlight landmarks and other points of interest. Yet, hand-designed maps are static representations that cannot adapt to the needs and tastes of the individual tourist. In this thesis we present an automated system for designing tourist maps that selects and highlights the information that is most important to tourists. Our system deter- mines the salience of map elements using bottom-up vision-based image analysis and top-down web-based information extraction techniques. It then generates a map that emphasizes the most important elements, using a combination of multiperspective ren- dering to increase visibility of streets and landmarks, and cartographic generalization techniques such as simplification, deformation, typification and displacement to em- phasize landmarks and de-emphasize less important buildings. We show a number of automatically generated tourist maps of San Francisco and compare them to existing automated and manual approaches. 3 4 Acknowledgments I would like to thank the people who supported me during my work on this thesis. I am particularly grateful to Maneesh Agrawala, my advisor at U.C. Berkeley. His experience and expertise in visualization are invaluable and his encouragement and support were crucial to the successful completion of this thesis. I would like to thank Maneesh for introducing me to research and encouraging me to pursue my own ideas. I look forward to continue working with him and pursue a Ph.D. at Berkeley. Many thanks to my ETH-advisor Mark Pauly and Bob Sumner for their helpful sug- gestions and numerous fruitful discussions. Mark was also the first person to teach me computer graphics. His encouragement and enthusiasm for graphics pushed me to pursue in this direction. Bob was always a great person to work with, not only for his helpful advice but also for his great sense of humour. Special thanks to Microsoft for providing us with the necessary data. Last but not least, I would like to thank the vendor from the corner store and the janitor of Soda Hall who often appeared more concerned about my deadline than I was and occasionally offered me late night snacks. I would also like to thank all the professors that have helped me along the way in my Bachelor and Master studies at ETH Zurich. I am deeply grateful to Hans Hinterberger who has always been very supportive and encouraging since my third semester at ETH. He valued my enthusiasm for school and pushed me to apply as a visiting scholar to Berkeley. Organizing my visit to Berkeley and extending my stay has been a true challenge. Especially Hans Hinterberger and Markus Gross, but also Mark Pauly and Walter Gander have made it possible. I am very grateful to Markus Gross for his invaluable guidance and advice on topics related to graduate school. I thank Hans Dubach for having contributed in making my studies challenging until the very end. ETH and Berkeley have been good places to work because of the many friends I have made. In particular I would like to thank: Hanni Wolf who has been a great comrade through the hardship of undergraduate school. By sharing endless discussions about movies, art and life, joining my sarcasm and cooking delicious Japanese meals, she has kept me smiling. Jason Sanders, my former office mate and coffee brother, for making our lab a great place to spend days in (and before deadlines nights). Rose-Line Werner and Moritz Baecher for the great teamwork in many course projects and their incessant optimism and support. 5 6 Contents 1 Introduction 13 2 Tourist Map Design 17 2.1 Important information . 17 2.2 Rendering . 18 3 System Overview 21 4 Computing Importance 23 4.1 Landmarks . 24 4.2 Roads . 29 4.3 Districts . 30 4.4 Nodes . 30 5 Rendering 31 5.1 Multiperspective Maps . 31 5.2 Generalization . 34 5.3 Non-Photorealistic Rendering . 40 5.4 Labeling . 41 6 Results and Conclusions 43 6.1 Generated Maps . 43 6.2 Comparison with Existing Maps . 51 6.3 Timing Information . 52 6.4 Conclusions . 56 7 8 CONTENTS List of Figures 1.1 Comparison between a digital map and a map generated by our system. 15 2.1 Examples of the five city elements. 18 2.2 An example of a multiperspective map. 19 3.1 The input data to our system. 22 4.1 Semantic landmark extraction. 25 4.2 Extraction of building color. 25 4.3 Shape complexity features. 26 4.4 Comparison of semantic, visual and structural landmarks in an area. 28 5.1 Oblique projection parameters. 32 5.2 Perspective Projection: Rotation of the buildings to maintain the align- ment between the buildings and the street. 33 5.3 Building Simplification. 34 5.4 Building Simplification: facet shifting. 35 5.5 Five steps of building simplification. 36 5.6 Matching Criterion for the Building Typification. 36 5.7 Typification applied to a block . 37 5.8 Alignment groups within a block. 38 5.9 Block Generalization. 40 6.1 Result: A combination of semantic landmarks in San Francisco. 45 6.2 Result: Tourist attraction map in San Francisco . 46 6.3 Result: Restaurant map in San Francisco . 47 6.4 Result: Shopping map in San Francisco . 48 6.5 Result: A map of the Market Street in San Francisco using an adaptive oblique projection for the landmarks. 49 6.6 Result: Generalization applied on the area around the Grace Cathedral in San Francisco. 50 6.7 Comparison of our Results with a Hand-Drawn Maps. 51 6.8 Comparison of our results with an existing automatically generated maps. 52 9 10 LIST OF FIGURES List of Tables 6.1 Variable Definition . 53 6.2 Timing Information of the Preprocessing Stages . 54 6.3 Timing Information of the Authoring Application . 55 11 12 LIST OF TABLES Chapter 1 Introduction Tourist maps are essential resources for visitors to an unfamiliar city because they visually highlight landmarks and other points of interest such as museums, restaurants, parks, and shopping districts. The most effective tourist maps are carefully designed to present this information so that visitors can easily navigate to the places they are most interested in. Yet, designing and rendering such tourist maps is a time-consuming process that requires expert map design skills. Moreover, hand-designed maps are static representations that cannot adapt to the needs and tastes of the individual tourist. Points of interest can differ significantly from per- son to person. While visually distinctive buildings and environmental features can serve as general-purpose landmarks, one tourist may be most interested in shopping, while another may want to see nearby restaurants. In designing a map, the first chal- lenge for mapmakers is to determine the importance of these elements to the tourists that will use the map. Tourists often use a combination of public transportation and walking to move from one place to another. Therefore, an effective tourist map must provide rich visual rep- resentations of landmarks and points of interest to help tourists quickly identify where they are located and determine the best (most interesting) route to their destination. For example, mapmakers use multi-perspective rendering to increase the visibility of streets and landmarks. Similarly, they use a variety of cartographic generalization tech- niques to increase the recognizability of landmarks and emphasize the most important elements in the map. Thus, the second challenge for mapmakers is to choose a set of rendering and cartographic generalization techniques that emphasize the most impor- tant landmarks and points of interest while de-emphasizing or eliminating irrelevant elements. In the last decade, digital maps such as those provided by Microsoft Live (www.live.com), Google Maps (maps.google.com) and Yahoo Maps (maps.yahoo.com) have become in- creasingly popular. Cities are usually in a perpetual state of construction, renovation and renewal in which streets and buildings are created, destroyed and reshaped. One 13 14 CHAPTER 1. INTRODUCTION advantage of such digital maps over hand-designed maps is that they are based on con- tinually updated geometric models of the streets and buildings in the city and therefore usually reflect the most up-to-date information. However, these digital maps do not adequately address the two primary challenges of creating tourist maps and suffer from several problems, including: Too much clutter. Digital maps do not identify the important landmarks and points of interest. Instead they display every street and building in the region, making it difficult for tourists to filter out the information they need and localize the important features of a city. Lack of organization/legibility. Digital maps fail to organize the map into distinct and easily recognizable units, such as landmarks, streets, blocks, and parks. As a consequence the layout of the city is complex to understand and tourists experience difficulties in wayfinding tasks. Poor visibility. Digital maps use a single perspective that makes it difficult to see both the street network and the buildings in a single view. In contrast, mapmakers often use multiple perspectives so that both the streets and the (visually salient) street-side facades of buildings are clearly visible.