Automated Pattern Detection and Generalization of Building Groups

This dissertation focuses on the topic of building group generalization by considering the detection of building patterns. Generalization is an important research field in cartography, which is part of map production and the basis for the derivation of multiple representation. As one of the most important features on map, buildings occupy large amount of map space and normally have complex shape and spatial distribution, which leads to that the generalization of buildings has long been an important and challenging task. For social, architectural and geographical reasons, the buildings were built with some special rules which forms different building patterns. Building patterns are crucial structures which should be carefully considered during graphical representation and generalization. Although people can effortlessly perceive these patterns, however, building patterns are not explicitly described in building datasets. Therefore, to better support the subsequent generalization process, it is important to automatically recognize building patterns. The objective of this dissertation is to develop effective methods to detect building patterns from building groups. Based on the identified patterns, some generalization methods are proposed to fulfill the task of building generalization. The main contribution of the dissertation is described as the following five aspects: (1) The terminology and concept of building pattern has been clearly explained; a detailed and relative complete typology of building patterns has been proposed by summarizing the previous researches as well as extending by the author; (2) A stroke-mesh based method has been developed to group buildings and detect different patterns from the building groups; (3) Through the analogy between line simplification and linear building group typification, a stroke simplification based typification method has been developed aiming at solving the generalization of building groups with linear patterns; (4) A mesh-based typification method has been developed for the generalization of the building groups with grid patterns; (5) A method of extracting hierarchical skeleton structures from discrete buildings have been proposed. The extracted hierarchical skeleton structures are regarded as the representations of the global shape of the entire region, which is used to control the generalization process. With the above methods, the building patterns are detected from the building groups and the generalization of building groups are executed based on the patterns. In addition, the thesis has also discussed the drawbacks of the methods and gave the potential solutions.:Abstract I Kurzfassung III Contents V List of Figures IX List of Tables XIII List of Abbreviations XIV Chapter 1 Introduction 1 1.1 Background and motivation 1 1.1.1 Cartographic generalization 1 1.1.2 Urban building and building patterns 1 1.1.3 Building generalization 3 1.1.4 Hierarchical property in geographical objects 3 1.2 Research objectives 4 1.3 Study area 5 1.4 Thesis structure 6 Chapter 2 State of the Art 8 2.1 Operators for building generalization 8 2.1.1 Selection 9 2.1.2 Aggregation 9 2.1.3 Simplification 10 2.1.4 Displacement 10 2.2 Researches of building grouping and pattern detection 11 2.2.1 Building grouping 11 2.2.2 Pattern detection 12 2.2.3 Problem analysis . 14 2.3 Researches of building typification 14 2.3.1 Global typification 15 2.3.2 Local typification 15 2.3.3 Comparison analysis 16 2.3.4 Problem analysis 17 2.4 Summary 17 Chapter 3 Using stroke and mesh to recognize building group patterns 18 3.1 Abstract 19 3.2 Introduction 19 3.3 Literature review 20 3.4 Building pattern typology and study area 22 3.4.1 Building pattern typology 22 3.4.2 Study area 24 3.5 Methodology 25 3.5.1 Generating and refining proximity graph 25 3.5.2 Generating stroke and mesh 29 3.5.3 Building pattern recognition 31 3.6 Experiments 33 3.6.1 Data derivation and test framework 33 3.6.2 Pattern recognition results 35 3.6.3 Evaluation 39 3.7 Discussion 40 3.7.1 Adaptation of parameters 40 3.7.2 Ambiguity of building patterns 44 3.7.3 Advantage and Limitation 45 3.8 Conclusion 46 Chapter 4 A typification method for linear building groups based on stroke simplification 47 4.1 Abstract 48 4.2 Introduction 48 4.3 Detection of linear building groups 50 4.3.1 Stroke-based detection method 50 4.3.2 Distinguishing collinear and curvilinear patterns 53 4.4 Typification method 55 4.4.1 Analogy of building typification and line simplification 55 4.4.2 Stroke generation 56 4.4.3 Stroke simplification 57 4.5 Representation of newly typified buildings 60 4.6 Experiment 63 4.6.1 Linear building group detection 63 4.6.2 Typification results 65 4.7 Discussion 66 4.7.1 Comparison of reallocating remained nodes 66 4.7.2 Comparison with classic line simplification method 67 4.7.3 Advantage 69 4.7.4 Further improvement 71 4.8 Conclusion 71 Chapter 5 A mesh-based typification method for building groups with grid patterns 73 5.1 Abstract 74 5.2 Introduction 74 5.3 Related work 75 5.4 Methodology of mesh-based typification 78 5.4.1 Grid pattern classification 78 5.4.2 Mesh generation 79 5.4.3 Triangular mesh elimination 80 5.4.4 Number and positioning of typified buildings 82 5.4.5 Representation of typified buildings 83 5.4.6 Resizing Newly Typified Buildings 85 5.5 Experiments 86 5.5.1 Data derivation 86 5.5.2 Typification results and evaluation 87 5.5.3 Comparison with official map 91 5.6 Discussion 92 5.6.1 Advantages 92 5.6.2 Further improvements 93 5.7 Conclusion 94 Chapter 6 Hierarchical extraction of skeleton structures from discrete buildings 95 6.1 Abstract 96 6.2 Introduction 96 6.3 Related work 97 6.4 Study area 99 6.5 Hierarchical extraction of skeleton structures 100 6.5.1 Proximity Graph Network (PGN) of buildings 100 6.5.2 Centrality analysis of proximity graph network 103 6.5.3 Hierarchical skeleton structures of buildings 108 6.6 Generalization application 111 6.7 Experiment and discussion 114 6.7.1 Data statement 114 6.7.2 Experimental results 115 6.7.3 Discussion 118 6.8 Conclusions 120 Chapter 7 Discussion 121 7.1 Revisiting the research problems 121 7.2 Evaluation of the presented methodology 123 7.2.1 Strengths 123 7.2.2 Limitations 125 Chapter 8 Conclusions 127 8.1 Main contributions 127 8.2 Outlook 128 8.3 Final thoughts 131 Bibliography 132 Acknowledgements 142 Publications 14

Verfahren zur Ableitung kleinerer Maßstäbe aus Daten der Digitalen Übersichtskarte der Stadt Dresden 1:25.000

Die kartographische Generalisierung ist eines der zentralen Themengebiete der Kartographie. Seit den 1960er Jahren vollzieht sich ein Entwicklungsprozess in der Generalisierung vom freien praktischen Generalisieren in Abhängigkeit von den Fähigkeiten des Kartenbearbeiters hin zur regelhaften rechengestützten Automation. Bis heute sind viele Fragen in Bezug auf die vollautomatische Generalisierung offen. Die vorliegende Arbeit widmet sich dieser Thematik und liefert einen Lösungsansatz für die automatische Ableitung der Daten des Städtischen Vermessungsamtes Dresden in 1:25.000 und des ATKIS Basis-DLMs in kleinere Folgemaßstäbe. Dabei werden die einzelnen Generalisierungsprozeduren und -abläufe im Einzelnen sowie in ihrer gesamten Komplexität betrachtet. Elementare Vorgänge beim Generalisieren, wie das Auswählen, Klassifizieren, Zusammenfassen, Überdimensionieren, Verdrängen und Vereinfachen (insbesondere Linienglättung) werden beschrieben und zu einem Gesamtablauf zusammengefügt. Der Schwerpunkt liegt hierbei auf der Flächenaggregation benachbarter Flächen unter Wahrung der topologischen Verhältnisse zu linienhaften Objekten. Das Ergebnis der Arbeit ist eine eigenständige Applikation, die zukünftig das Städtische Vermessungsamt Dresden bei der Laufendhaltung seiner Daten unterstützen wird.:Kurzfassung IV Abstract V Inhaltsverzeichnis VI Abbildungsverzeichnis IX Tabellenverzeichnis X Abkürzungsverzeichnis XII 1 Einleitung und Motivation 1 1.1 Allgemeine Einführung 1 1.2 Ziele und Abgrenzung der Arbeit 2 2 Grundlagen der Generalisierung 3 2.1 Generalisierung in der Kartographie 3 2.2 Kartographische Modelltheorie und Modellbildung im digitalen Umfeld 5 2.3 Modell- und kartographische Generalisierung 7 2.4 Rahmenmodelle der digitalen Generalisierung 8 2.4.1 Brassel-Weibel-Modell 9 2.4.2 McMaster-Shea-Modell 10 2.5 Elementare Generalisierungsvorgänge 11 2.5.1 Klassifikation 15 2.5.2 Flächenaggregation 15 2.5.3. Flächenexpansion und -reduktion durch morphologische Operatoren 16 2.5.4 Verdrängung 18 2.5.4.1 Nickerson-Algorithmus 19 2.5.4.2 Linienverdrängung mittels Energieminimierung 22 2.5.5 Geometrietypwechsel 26 2.5.5.1 Dimensionswechsel von Fläche zu Linie 27 Inhaltsverzeichnis 2.5.5.2 Dimensionswechsel von Fläche zu Punkt 29 2.5.6 Linienvereinfachung und Linienglättung 30 2.5.6.1 Douglas-Algorithmus 31 2.5.6.2 McMaster-Algorithmus 32 2.5.7 Generalisierungsoperatoren in ausgewählten kommerziellen GIS 33 3 Datenmodelle der Digitalen Übersichtskarte der Stadt Dresden 35 3.1 Datenmodelle des Städtischen Vermessungsamtes Dresden 35 3.1.1 Erweitertes Straßenknotennetz (ESKN) 35 3.1.2 Erweiterte Blockkarte (EBK) 36 3.2 ATKIS Basis-DLM 38 4 Generalisierungsverfahren und Parametrisierung 40 4.1 Anforderungen an das Generalisierungsergebnis und -verfahren 40 4.2 Gesamtablauf des Generalisierungsverfahren 41 4.3 Algorithmen- und Parameterwahl für die Generalisierungsoperatoren 44 4.3.1 Anpassung der Selektion 44 4.3.2 Anpassung der Klassifikation 47 4.3.3 Anpassung der Zusammenfassung 48 4.3.4 Anpassung der Überzeichnung 51 4.3.5 Anpassung des Geometrietypwechsels 52 4.3.6 Anpassung der Linienglättung und Linienvereinfachung 54 4.3.7 Anpassung der Verdrängung 56 5 Programmtechnische Umsetzung 58 5.1 Weiterentwicklungsmöglichkeiten von GIS-Applikationen durch objektorientiertes Programmieren 58 5.2 ArcObjects und FMEObjects 60 5.3 Programmaufbau 61 5.3.1 Oberflächengestaltung 61 5.3.2 Module 62 5.3.3 Prozeduren 64 5.3.3.1 Button1_Click 64 5.3.3.2 Prozeduren des Modules FMEObj 66 Inhaltsverzeichnis 5.3.3.3 Klassifikationsprozeduren 67 5.3.3.4 Zusammenfassungsprozeduren 70 5.3.3.5 Vergrößerungsprozedur 71 5.3.3.6 Morphologieprozeduren 72 5.3.3.7 Geometrietypwechsel- und Flächenaggregationsprozeduren 73 5.3.3.8 Kantenmodellprozedur 80 5.3.3.9 Punktsignaturenableitungsprozeduren 81 5.3.3.10 Linienglättungs- und Punktneuorientierungsprozeduren 82 5.4 Tabellen der Parameterdatenbank und ihre Strukturen 84 5.5 Handlungsanweisungen für den Nutzer 87 6 Evaluation der Ergebnisse 89 6.1 Evaluierung zur Nachbearbeitung 90 6.2 Numerisch beschreibende Evaluierung 91 6.3 Evaluierung zur Gütebestimmung 94 6.4 Individuelle subjektive Bewertung 96 6.5 Vergleich mit der amtlich-topographischen Karte 98 7 Fehleranalyse und Dokumentation interaktiv zu lösender Konflikte 100 8 Zusammenfassung und Ausblick 102 Literaturverzeichnis 104 Anhangsverzeichnis 110Cartographic generalization is one of the most pivotal issues in cartography. From the 1960s on, a development from free practical map generalization depending on the abilities of the mapmaker towards a scale-determined computer assisted automation has taken place. By today, many open questions concerning the entirely automatic generalization are still remaining. This thesis addresses the issue of automatic generalization and provides a solution for the automatic derivation of data from Dresden’s Municipal Survey Office in 1:25.000 and ATKIS Base DLM into smaller scales. The generalization procedures will be considered both in detail and as a whole. Elementary generalization procedures, such as selection, classification, regrouping, amplification, displacement and simplification (particularly line smoothing) will be described and combined to form a complete process. The focus is set on aggregation of adjacent areas, while maintaining the topological relationship to line objects. The result is a stand-alone application being capable of supporting Dresden’s Municipal Survey Office in revising its data.:Kurzfassung IV Abstract V Inhaltsverzeichnis VI Abbildungsverzeichnis IX Tabellenverzeichnis X Abkürzungsverzeichnis XII 1 Einleitung und Motivation 1 1.1 Allgemeine Einführung 1 1.2 Ziele und Abgrenzung der Arbeit 2 2 Grundlagen der Generalisierung 3 2.1 Generalisierung in der Kartographie 3 2.2 Kartographische Modelltheorie und Modellbildung im digitalen Umfeld 5 2.3 Modell- und kartographische Generalisierung 7 2.4 Rahmenmodelle der digitalen Generalisierung 8 2.4.1 Brassel-Weibel-Modell 9 2.4.2 McMaster-Shea-Modell 10 2.5 Elementare Generalisierungsvorgänge 11 2.5.1 Klassifikation 15 2.5.2 Flächenaggregation 15 2.5.3. Flächenexpansion und -reduktion durch morphologische Operatoren 16 2.5.4 Verdrängung 18 2.5.4.1 Nickerson-Algorithmus 19 2.5.4.2 Linienverdrängung mittels Energieminimierung 22 2.5.5 Geometrietypwechsel 26 2.5.5.1 Dimensionswechsel von Fläche zu Linie 27 Inhaltsverzeichnis 2.5.5.2 Dimensionswechsel von Fläche zu Punkt 29 2.5.6 Linienvereinfachung und Linienglättung 30 2.5.6.1 Douglas-Algorithmus 31 2.5.6.2 McMaster-Algorithmus 32 2.5.7 Generalisierungsoperatoren in ausgewählten kommerziellen GIS 33 3 Datenmodelle der Digitalen Übersichtskarte der Stadt Dresden 35 3.1 Datenmodelle des Städtischen Vermessungsamtes Dresden 35 3.1.1 Erweitertes Straßenknotennetz (ESKN) 35 3.1.2 Erweiterte Blockkarte (EBK) 36 3.2 ATKIS Basis-DLM 38 4 Generalisierungsverfahren und Parametrisierung 40 4.1 Anforderungen an das Generalisierungsergebnis und -verfahren 40 4.2 Gesamtablauf des Generalisierungsverfahren 41 4.3 Algorithmen- und Parameterwahl für die Generalisierungsoperatoren 44 4.3.1 Anpassung der Selektion 44 4.3.2 Anpassung der Klassifikation 47 4.3.3 Anpassung der Zusammenfassung 48 4.3.4 Anpassung der Überzeichnung 51 4.3.5 Anpassung des Geometrietypwechsels 52 4.3.6 Anpassung der Linienglättung und Linienvereinfachung 54 4.3.7 Anpassung der Verdrängung 56 5 Programmtechnische Umsetzung 58 5.1 Weiterentwicklungsmöglichkeiten von GIS-Applikationen durch objektorientiertes Programmieren 58 5.2 ArcObjects und FMEObjects 60 5.3 Programmaufbau 61 5.3.1 Oberflächengestaltung 61 5.3.2 Module 62 5.3.3 Prozeduren 64 5.3.3.1 Button1_Click 64 5.3.3.2 Prozeduren des Modules FMEObj 66 Inhaltsverzeichnis 5.3.3.3 Klassifikationsprozeduren 67 5.3.3.4 Zusammenfassungsprozeduren 70 5.3.3.5 Vergrößerungsprozedur 71 5.3.3.6 Morphologieprozeduren 72 5.3.3.7 Geometrietypwechsel- und Flächenaggregationsprozeduren 73 5.3.3.8 Kantenmodellprozedur 80 5.3.3.9 Punktsignaturenableitungsprozeduren 81 5.3.3.10 Linienglättungs- und Punktneuorientierungsprozeduren 82 5.4 Tabellen der Parameterdatenbank und ihre Strukturen 84 5.5 Handlungsanweisungen für den Nutzer 87 6 Evaluation der Ergebnisse 89 6.1 Evaluierung zur Nachbearbeitung 90 6.2 Numerisch beschreibende Evaluierung 91 6.3 Evaluierung zur Gütebestimmung 94 6.4 Individuelle subjektive Bewertung 96 6.5 Vergleich mit der amtlich-topographischen Karte 98 7 Fehleranalyse und Dokumentation interaktiv zu lösender Konflikte 100 8 Zusammenfassung und Ausblick 102 Literaturverzeichnis 104 Anhangsverzeichnis 11

Categorical database generalization in GIS

Key words: Categorical database, categorical database generalization, Formal data structure, constraints, transformation unit, classification hierarchy, aggregation hierarchy, semantic similarity, data model, Delaunay triangulation network. semantic similarity evaluation model.Categorical databases are widely used in GIS for different kinds of application, analysis, planning, evaluation and management. Database generalization that derives different resolution databases from a single database with more detail is one of the key research problems and a hot research point in the GIS and Cartography field. This dissertation presents a framework for categorical database generalization in GIS. It includes defining conceptual aspects of current categorical database generalization transformation and constraints for generalization transformation, elaboration on supporting data structure and transformation units, development of auxiliary analysis methods, and demonstration of some application examples.Database generalization is considered as a transformation process. Three kinds of transformation are defined based on the characteristics of categorical database and categorical database generalization. They are geo-spatial model transformation, object transformation and relation transformation. Each transformation has a certain function and deals with some aspects of database. Geo-spatial transformation is mainly used to define the content framework of a new database and decide the theme of a new database. Object transformation and relation transformation deal with transformations of thematic and geometric aspects of objects and relationship between objects from an existing database to a new database.Database generalization (transformation) requires a data structure that strongly supports data organization, spatial analysis and decision-making in a database. The design of a data structure should take two functions into account. One provides the basis for describing and organizing spatial objects and the relationships between them. and the other is for analyzing and supporting operations on spatial objects. This thesis introduces the IEFDS, an integrated and extended version of FDS, as a data model to support automated database generalization transformation. The addition to FDS is triangles. The triangles and their classification are proposed based on constituent properties of triangles in IEFDS which plays an important role in the extended adjacent and inclusion relations and extracting the skeleton line. Some examples of spatial query operations that make use of the extended adjacent relation and semantic triangles are also provided in this thesis.In a categorical database, similarity between object types can be described by a similarity measure. The similarity is application-dependent. In a sense, the similarity will control and guide database transformation operations. The similarity evaluation model and similarity matrix are proposed for analyzing and representing similarity between objects and object types in this study which is based on Set-theory, classification and aggregation hierarchy. The constraints such as transformation conditions play a key role in the process of databasegeneralization. Constraints can be used to identify conflicting areas, guide choices of operationsand trigger operations as well as govern the database generalization. The processes of generalization should be performed by a series of operations under the control of constraints. Three types of constraints, data model. object and relationships based on an object-oriented database are proposed in landuse database generalization. These constraints can be specified interactively by users and varied to reflect different objectives or purposes. These types of constraints are applicationdependent. This will make the database generalization process very flexible/adaptive, and the decisionmaking can be based on geographic meaning and not simply on the geometry of an object.An important element proposed in this study is the transformation unit. It is an important process unit as many generalization problems need to be solved by considering a subset of related objects as a whole, rather than treating them individually. In a sense, the transformation unit is a basic analysis. processing, decision-making unit and a trigger to aggregation operation processes and it plays an important role in database transformation. The conflicted objects and its (their) related objects are organized into a transformation unit. A transformation unit that "brings together- a subset of objects can he created by conflict,; in thematic and /or geometric aspects of objects or spatial relation among objects or integrating them. The main purpose of creating a transformation unit is for the preparationofan aggregation operation. It limits the area and numberofa setofrelated objects in an aggregation operation. The different conflict types will create different types of transformation units. For this study, four types of transformation units are considered based on the constraints discussed. Each of which has a corresponding aggregation operation.The auxiliary analysis methods (algorithms) are needed to actually perform spatial analysis and transformations. The most fundamental tasks are to identify where to generalize, how to generalize, and when to generalize. The thesis introduces a number of auxiliary analysis methods that have been developed to solve a number of important geometric and thematic problems in database trans, form ati on. These auxiliary analysis methods include semantic similarity rnatrix, computing a model of similarity, detection and creation of transformation units, area object aggregation analysis and the process based on transformation units, multineighborhood, object cluster and creation of catchments hierarchy etc.Such examples of the application are included in the thesis as object cluster, land use aggregation and automated organization of hierarchical catchments. The application examples demonstrate the applicability and benefits of the IEFDS and similarity evaluation model. These supporting models play a key role in organizing thematic and geometric data, spatial analysis and spatial query in database generalization. It also proved that a lot of critical geometric and thematic problems in database generalization can be solved, or can be solved in a more efficient way, with the support of an adequate data model.</font

An investigation into automated processes for generating focus maps

The use of geographic information for mobile applications such as wayfinding has increased rapidly, enabling users to view information on their current position in relation to the neighbouring environment. This is due to the ubiquity of small devices like mobile phones, coupled with location finding devices utilising global positioning system. However, such applications are still not attractive to users because of the difficulties in viewing and identifying the details of the immediate surroundings that help users to follow directions along a route. This results from a lack of presentation techniques to highlight the salient features (such as landmarks) among other unique features. Another problem is that since such applications do not provide any eye-catching distinction between information about the region of interest along the route and the background information, users are not tempted to focus and engage with wayfinding applications. Although several approaches have previously been attempted to solve these deficiencies by developing focus maps, such applications still need to be improved in order to provide users with a visually appealing presentation of information to assist them in wayfinding. The primary goal of this research is to investigate the processes involved in generating a visual representation that allows key features in an area of interest to stand out from the background in focus maps for wayfinding users. In order to achieve this, the automated processes in four key areas - spatial data structuring, spatial data enrichment, automatic map generalization and spatial data mining - have been thoroughly investigated by testing existing algorithms and tools. Having identified the gaps that need to be filled in these processes, the research has developed new algorithms and tools in each area through thorough testing and validation. Thus, a new triangulation data structure is developed to retrieve the adjacency relationship between polygon features required for data enrichment and automatic map generalization. Further, a new hierarchical clustering algorithm is developed to group polygon features under data enrichment required in the automatic generalization process. In addition, two generalization algorithms for polygon merging are developed for generating a generalized background for focus maps, and finally a decision tree algorithm - C4.5 - is customised for deriving salient features, including the development of a new framework to validate derived landmark saliency in order to improve the representation of focus maps