Surface networks

© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou

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

Context awareness and typification in building generalisation

The objective of this thesis is the development of an automated process to perform the generalisation of buildings from 1:5000 to 1:50000 scale. The strategy adopted is applied to partitions of the dataset (blocks) and differs between urban and rural context; ad-hoc typifcation algorithms have been developed to cope with high-density blocks, medium-density blocks and spatial patterns. Low-density blocks that do not fitt the previous classifications are treated with a best-effort approachope

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

Harmonization of categorical maps by alignment processes and thematic consistency analysis

Nicolau, R., Basos, N., Marcelino, F., Caetano, M., & M. C. Pereira, J. (2020). Harmonization of categorical maps by alignment processes and thematic consistency analysis. AIMS Geosciences, 6(4), 473-490. https://doi.org/10.3934/geosci.2020026This paper describes an approach for harmonizing historical vector categorical maps with related modern maps. The approach aims at the correction of geometric distortions and semantic disagreements using alignment processes and analysis of thematic coherence. The harmonized version of the map produced by this approach can already be overlaid with other maps, what was unfeasible with the original map. The positional errors of the old map are reduced by two consecutive geometric adjustments, which use transformations usually available in most GIS software. The thematic consistency between the old and the modern map is achieved by harmonizing their classification systems and by the inclusion of specific contents missing in the early map, but represented in the modern map (e.g. small rivers). This approach was tested in the geometric and thematic harmonization of the Portuguese Land Cover/Land Use (LCLU) map for 1990 (COS90). In this test, the 1995 orthorectified aerial images and the 1995 LCLU map (COS95) were used as reference sources of higher positional accuracy, to align the COS90 map. COS90 was firstly adjusted with the 1995 aerial images by an NTV2 grid transformation, developed by the authors. Then, for reduction of the local distortions, the map resulting from the first transformation was aligned with the COS95 by a rubber-sheeting linear interpolation transformation. This geometric harmonization enabled a decrease of the Root Mean Square Error of COS90 from 204 meters to 13 meters. The thematic harmonization of COS90 enabled its comparison with modern related maps, and the integration of 201 river sections, that were missing because the specifications used in the production of the original map did not allow their representation.publishersversionpublishe

Совместное упрощение пространственных объектов различного типа с сохранением топологических отношений

Cartographic generalization includes the process of graphically reducing information from reality or larger scaled maps to display only the information that is necessary at a specific scale. After generalization, maps can show the main things and essential characteristics. The scale, use and theme of maps, geographical features of cartographic regions and graphic dimensions of symbols are the main factors affecting cartographic generalization. Geometric simplification is one of the core components of cartographic generalization. The topological relations of spatial features also play an important role in spatial data organization, queries, updates, and quality control. Various map transformations can change the relationships between features, especially since it is common practice to simplify each type of spatial feature independently (first administrative boundaries, then road network, settlements, hydrographic network, etc.). In order to detect the spatial conflicts a refined description of topological relationships is needed. Considering coverings and mesh structures allows us to reduce the more general problem of topological conflict correction to the problem of resolving topological conflicts within a single mesh cell. In this paper, a new simplification algorithm is proposed. Its peculiarity is the joint simplification of a set of spatial objects of different types while preserving their topological relations. The proposed algorithm has a single parameter — the minimum map detail size (usually it is equal to one millimeter in the target map scale). The first step of the algorithm is the construction of a special mesh data structure. On its basis for each spatial object a sequence of cells is formed, to which points of this object belong. If a cell contains points of only one object, its geometric simplification is performed within the bounding cell using the sleeve-fitting algorithm. If a cell contains points of several objects, geometric simplification is performed using a special topology-preserving procedure.Картографическая генерализация включает выбор отображаемых на карте объектов и явлений и их упрощение (обобщение) с сохранением основных типичных черт и характерных особенностей, а также взаимосвязей в соответствии с критериями, задаваемыми в запросе пользователем, в том числе решаемой задачей и масштабом отображаемой карты. Различные преобразования карт могут изменить отношения между объектами, тем более что общепринятой является практика упрощения каждого типа пространственных объектов независимо (сначала административные границы, потом дорожная сеть, населенные пункты, гидрографическая сеть и т. д.). Разрешение топологических конфликтов — одна из важнейших задач цифровой генерализации карт, решению которой уделяется особое внимание с начала исследований в этой области. Рассмотрение покрытий и сеточных структур позволяет свести более общую проблему коррекции топологических конфликтов к задаче разрешения топологических конфликтов внутри одной ячейки сетки. В настоящей работе предлагается новый алгоритм геометрического упрощения. Его особенностью является совместное упрощение множества пространственных объектов различного типа с сохранением их топологических отношений. Предлагаемый алгоритм имеет единственный параметр минимальный размер отображаемой на карте детали (обычно он равен одному миллиметру в целевом масштабе карты). Первым шагом алгоритма является построение специальной сеточной структуры данных. На ее основе для каждого пространственного объекта формируется последовательность ячеек, которым принадлежат точки данного объекта. Если в ячейке находятся точки только одного объекта, то его геометрическое упрощение происходит в рамках ограничивающей ячейки по алгоритму sleeve-fitting. Если в ячейке содержатся точки нескольких объектов, то геометрическое упрощение осуществляется с помощью специальной, сохраняющей топологию, процедуры