3D Cadastres Best Practices, Chapter 5: Visualization and New Opportunities

This paper proposes a discussion on opportunities offered by 3D visualization to improve the understanding and the analysis of cadastre data. It first introduce the rationale of having 3D visualization functionalities in the context of cadastre applications. Second the publication outline some basic concepts in 3D visualization. This section specially addresses the visualization pipeline as a driven classification schema to understand the steps leading to 3D visualization. In this section is also presented a brief review of current 3D standards and technologies. Next is proposed a summary of progress made in the last years in 3D cadastral visualization. For instance, user’s requirement, data and semiotics, and platforms are highlighted as main actions performed in the development of 3D cadastre visualization. This review could be perceived as an attempt to structure and emphasise the best practices in the domain of 3D cadastre visualization and as an inventory of issues that still need to be tackled. Finally, by providing a review on advances and trends in 3D visualization, the paper initiates a discussion and a critical analysis on the benefit of applying these new developments to cadastre domain. This final section discusses about enhancing 3D techniques as dynamic transparency and cutaway, 3D generalization, 3D visibility model, 3D annotation, 3D data and web platform, augmented reality, immersive virtual environment, 3D gaming, interaction techniques and time

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

The rendering of virtual three-dimensional (3D) structures represented by Point Cloud (PC) allows the representation of internal and/or external environments to buildings. However, the compilation of 3D geometric models is influenced by the intrinsic characteristics of PCs, which can be mitigated by the application of an PC simplification operator. According to the mathematical norms of fractal geometry, it was assumed that a PC is characterized by self-similarity. Two experimental datasets acquired with an SLT in static mode indoors were used. Four tasks were accomplished: sampling and structuring of a PC to solve the problem of random distribution, from an octree structure; estimation of the curvature of the points and the roughness of a neighbourhood for the extraction of edge points by the analysis of self-similarity and application of the Statistical Outliers Remove (SOR) algorithm, for the elimination of outliers points; uniform voxelization, to simplify the intermediate points; application of the Iterative Closest Point (ICP) algorithm to register the sets generated in the same local coordinate system. The use of voxelization was satisfactory, but once the voxel size is manually defined, the PC can be oversimplified and lose essential characteristics. This can be minimized by the primary analysis of the edge points, generating a set that is uniform, less noisy, and self- similar to the original set. To achieve a minimum density of points to model an environment three-dimensionally, one must analyse the geometric self- similarity characteristics of the PC to produce a simplified set self-similar to the original, considering the premises of fractal geometry. It is recommended to create an automatic simplification process to minimize the subjectivity coming from the analyst

Le nuage de point intelligent

Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. E.g., we can use such data as a reference for autonomous cars and robot’s navigation, as a layer for floor-plan’s creation and building’s construction, as a digital asset for environment modelling and incident prediction... Applications are numerous, and potentially increasing if we consider point clouds as digital reality assets. Yet, this expansion faces technical limitations mainly from the lack of semantic information within point ensembles. Connecting knowledge sources is still a very manual and time-consuming process suffering from error-prone human interpretation. This highlights a strong need for domain-related data analysis to create a coherent and structured information. The thesis clearly tries to solve automation problematics in point cloud processing to create intelligent environments, i.e. virtual copies that can be used/integrated in fully autonomous reasoning services. We tackle point cloud questions associated with knowledge extraction – particularly segmentation and classification – structuration, visualisation and interaction with cognitive decision systems. We propose to connect both point cloud properties and formalized knowledge to rapidly extract pertinent information using domain-centered graphs. The dissertation delivers the concept of a Smart Point Cloud (SPC) Infrastructure which serves as an interoperable and modular architecture for a unified processing. It permits an easy integration to existing workflows and a multi-domain specialization through device knowledge, analytic knowledge or domain knowledge. Concepts, algorithms, code and materials are given to replicate findings and extend current applications.Les ensembles discrets de données spatiales, appelés nuages de points, forment souvent le support principal pour des scénarios d’aide à la décision. Par exemple, nous pouvons utiliser ces données comme référence pour les voitures autonomes et la navigation des robots, comme couche pour la création de plans et la construction de bâtiments, comme actif numérique pour la modélisation de l'environnement et la prédiction d’incidents... Les applications sont nombreuses et potentiellement croissantes si l'on considère les nuages de points comme des actifs de réalité numérique. Cependant, cette expansion se heurte à des limites techniques dues principalement au manque d'information sémantique au sein des ensembles de points. La création de liens avec des sources de connaissances est encore un processus très manuel, chronophage et lié à une interprétation humaine sujette à l'erreur. Cela met en évidence la nécessité d'une analyse automatisée des données relatives au domaine étudié afin de créer une information cohérente et structurée. La thèse tente clairement de résoudre les problèmes d'automatisation dans le traitement des nuages de points pour créer des environnements intelligents, c'est-àdire des copies virtuelles qui peuvent être utilisées/intégrées dans des services de raisonnement totalement autonomes. Nous abordons plusieurs problématiques liées aux nuages de points et associées à l'extraction des connaissances - en particulier la segmentation et la classification - la structuration, la visualisation et l'interaction avec les systèmes cognitifs de décision. Nous proposons de relier à la fois les propriétés des nuages de points et les connaissances formalisées pour extraire rapidement les informations pertinentes à l'aide de graphes centrés sur le domaine. La dissertation propose le concept d'une infrastructure SPC (Smart Point Cloud) qui sert d'architecture interopérable et modulaire pour un traitement unifié. Elle permet une intégration facile aux flux de travail existants et une spécialisation multidomaine grâce aux connaissances liée aux capteurs, aux connaissances analytiques ou aux connaissances de domaine. Plusieurs concepts, algorithmes, codes et supports sont fournis pour reproduire les résultats et étendre les applications actuelles.Diskrete räumliche Datensätze, so genannte Punktwolken, bilden oft die Grundlage für Entscheidungsanwendungen. Beispielsweise können wir solche Daten als Referenz für autonome Autos und Roboternavigation, als Ebene für die Erstellung von Grundrissen und Gebäudekonstruktionen, als digitales Gut für die Umgebungsmodellierung und Ereignisprognose verwenden... Die Anwendungen sind zahlreich und nehmen potenziell zu, wenn wir Punktwolken als Digital Reality Assets betrachten. Allerdings stößt diese Erweiterung vor allem durch den Mangel an semantischen Informationen innerhalb von Punkt-Ensembles auf technische Grenzen. Die Verbindung von Wissensquellen ist immer noch ein sehr manueller und zeitaufwendiger Prozess, der unter fehleranfälliger menschlicher Interpretation leidet. Dies verdeutlicht den starken Bedarf an domänenbezogenen Datenanalysen, um eine kohärente und strukturierte Information zu schaffen. Die Arbeit versucht eindeutig, Automatisierungsprobleme in der Punktwolkenverarbeitung zu lösen, um intelligente Umgebungen zu schaffen, d.h. virtuelle Kopien, die in vollständig autonome Argumentationsdienste verwendet/integriert werden können. Wir befassen uns mit Punktwolkenfragen im Zusammenhang mit der Wissensextraktion - insbesondere Segmentierung und Klassifizierung - Strukturierung, Visualisierung und Interaktion mit kognitiven Entscheidungssystemen. Wir schlagen vor, sowohl Punktwolkeneigenschaften als auch formalisiertes Wissen zu verbinden, um schnell relevante Informationen mithilfe von domänenzentrierten Grafiken zu extrahieren. Die Dissertation liefert das Konzept einer Smart Point Cloud (SPC) Infrastruktur, die als interoperable und modulare Architektur für eine einheitliche Verarbeitung dient. Es ermöglicht eine einfache Integration in bestehende Workflows und eine multidimensionale Spezialisierung durch Gerätewissen, analytisches Wissen oder Domänenwissen. Konzepte, Algorithmen, Code und Materialien werden zur Verfügung gestellt, um Erkenntnisse zu replizieren und aktuelle Anwendungen zu erweitern

A Formalized 3D Geovisualization Illustrated to Selectivity Purpose of Virtual 3D City Model

Virtual 3D city models act as valuable central information hubs supporting many aspects of cities, from management to planning and simulation. However, we noted that 3D city models are still underexploited and believe that this is partly due to inefficient visual communication channels across 3D model producers and the end-user. With the development of a formalized 3D geovisualization approach, this paper aims to support and make the visual identification and recognition of specific objects in the 3D models more efficient and useful. The foundation of the proposed solution is a knowledge network of the visualization of 3D geospatial data that gathers and links mapping and rendering techniques. To formalize this knowledge base and make it usable as a decision-making system for the selection of styles, second-order logic is used. It provides a first set of efficient graphic design guidelines, avoiding the creation of graphical conflicts and thus improving visual communication. An interactive tool is implemented and lays the foundation for a suitable solution for assisting the visualization process of 3D geospatial models within CAD and GIS-oriented software. Ultimately, we propose an extension to OGC Symbology Encoding in order to provide suitable graphic design guidelines to web mapping services

Modelling and verifying land-use regulations comprising 3D components to detect spatio-semantic conflicts

L'utilisation du territoire est régie par différents mécanismes que nous pourrions nommer géorèglementations comme par exemples les plans d'urbanisme, les permis de construire ou le zonage. La géorèglementation, en anglais, on parle de Land-use Regulation (LuR), permet d'imposer ou d'influencer l'utilisation d'un territoire dans le but d'atteindre des objectifs de politique publique. Qu'on le veule ou pas, la géorèglementation est nécessaire car elle permet de consolider une saine gestion des ressources, elle aide à la conservation et au développement du territoire, elle fournit un cadre législatif important pour assurer la sécurité et le bon fonctionnement pour l'accès et l'utilisation harmonieuse du territoire. La géorèglementation s'applique donc sur un territoire, où les composantes spatiales, comme la géométrie des éléments, sont primordiales. Il faudra par exemple tenir compte des marges de recul (donc distance) lors de la construction d'une maison, d'une superficie maximale de construction, etc. Ces composantes spatiales du territoire et son occupation peuvent également faire intervenir la 3e dimension comme la profondeur, la hauteur ou encore le volume. La pratique et la littérature montrent que la géorèglementation est actuellement principalement décrite dans des documents de planification et des lignes directrices, dont certains peuvent inclure une représentation spatiale en 2D (i.e. des cartes). On retrouve parfois de coupes transversales en 2D pour représenter l'étendue 2D/3D des LuRs. Cette manière de travailler à partir de document manuscrit et de plans 2D présente des lacunes importantes. Elle limite la possibilité d'avoir une compréhension complète et adéquate de l'étendue 3D des LuRs et donc dans la prise de décision, comme par exemple, la détection de conflits potentiels dans la délivrance de permis de construire ou d'aménagement. De plus, l'application et donc la validation de ces géorèglementations à partir de documents descriptifs prend du temps et laisse place à la subjectivité, ce qui peut conduire à de mauvaises décisions. Les autorités en matière de planification territoriale devraient avoir accès à toutes les informations et à toutes les représentations spatiales requises pour évaluer les LuRs et détecter les conflits potentiels. Force est de constater, que ce n'est pas le cas actuellement, et que même si des modèles 3D de bâtiments (BIM) ou de ville (CityGML) ont vu le jour, ils ne sont pas intégrés dans ces processus de géorèglementation. Cette recherche doctorale est dédiée à la conception et au développement d'un cadre de référence pour la modélisation géométrique 3D des LuRs, leur intégration dans le contexte des modèles de ville 3D et la détection automatique des conflits spatio-sémantiques potentiels lors de la validation des LuRs. Ce cadre de référence vise donc à soutenir les autorités en matière d'application de géorèglementations. La recherche se décline en cinq sous-objectifs soit 1) proposer un inventaire des différents LuRs 3D en précisant leurs composantes 3D/verticales, 2) proposer une classification fonctionnelle basée sur l'ampleur des conflits potentiels des LuRs 3D pour soutenir la prise de décision des autorités, 3) modéliser les LuRs en 3D puis les combiner avec d'autres sources d'information (ex. BIM, CityGML et cartes de zonage), 4) détecter les conflits spatiaux et sémantiques potentiels qui pourraient survenir entre les LuRs modélisés et les objets physiques comme les éléments de construction et, 5) concevoir et développer une preuve de faisabilité. Parmi plus de 100 de géorèglementations 2D/3D passés en revue, 18 de géorèglementations 3D sont inventoriées et discutées en profondeur. Par la suite, pour chacune de ces géorèglementations, les informations et paramètres requis pour leur modélisation 3D automatique sont établis. L'approche proposée permet l'intégration de la modélisation 3D de ces géorèglementations à des modèles de villes et de bâtiments 3D (par exemple, BIM, CityGML et le zonage). Enfin, la thèse fournie un cadre procédurale pour vérifier automatiquement si les géorèglementations 3D viennent en conflit avec des éléments de bâtis planifiés. La preuve de faisabilité est un prototype Web basée sur une étude de cas axée sur le processus d'émission de permis de construire d'un bâtiment situé dans la ville de Melbourne, Victoria, Australie. Les géorèglementations 3D suivantes ont été modélisées et vérifiées : 1) limites de construction en hauteur, 2) exposition au soleil pour estimer l'efficacité énergétique du bâtiment, 3) limite des zones d'ombrage, 4) limites de l'impact sonore, 5) zonage de vue, 6) marges latérales et arrières, 7) marges de rue (côtés et frontaux), et 8) limites d'inondation.The use and developments of land are regulated by utilising different mechanisms called Land-use Regulation (LuR) in various forms such as planning activities, zoning codes, permit requirements, or subdivision controls of cities. LuR makes it possible to impose or influence the use and development of land in order to achieve public policy objectives. Indeed, LuR is essential since it allows the appropriate reinforcement of resource management, contributes to the land protection and development, and provides a tangible legal framework to ensure safety and proper functioning for the harmonious access and use of land. LuRs applies to land, where the spatial components, such as the geometry of the elements, are essential. For example, setback and height limits (i.e., the distance) or different floors' gross area should be considered when owners/developers propose a new construction on their property. These spatial components of the land, its occupied elements (e.g., building elements), or LuR itself can comprise the third dimension (i.e., depth, height, or even volume). Literature and related works show that LuR is currently mainly described in planning documents and guidelines, some of which may include 2D spatial representation (i.e., maps) or 2D cross-sections to represent the LuRs' 2D/3D extent. This method (i.e., working on textual documents and 2D plans) has significant shortcomings in understanding the LuRs' 3D extent and in decision-making (e.g., detecting potential conflicts in issuing planning/building permits). Moreover, checking LuRs' descriptions inside the textual documents is time-consuming, and subjective which might lead to erroneous decisions. Planning authorities need to have access to all information and the spatial representation that is required to assess LuRs and detect their potential conflicts. Clearly, it is generally lacking and even if 3D models of buildings (e.g., BIM designs) or cities (e.g., CityGML) have emerged, they do not incorporate the concept of LuRs. This Ph.D. research follows qualitative engineering type of method that generally aims to propose a conceptual framework for modelling 3D LuRs geometrically as part of 3D city models and formalising geometric and semantic requirements for detecting LuRs' potential conflicts automatically to support planning authorities in the statuary planning phase. To achieve the general objective, five specific objectives are defined as: 1) to formulate an inventory of various 3D LuRs specifying their 3D/vertical components, 2) to propose a functional classification based on the magnitude of 3D LuRs' potential conflicts for supporting planning authorities' decision-making goals, 3) to model LuRs in 3D and then combine them with other sources of information (e.g., BIM, city models, and zoning maps), 4) to automate the detection of potential spatio-semantic conflicts that might arise between the modelled LuRs and physical objects like building elements, and 5) to design and develop proof of feasibility for modelling and verifying 3D LuRs automatically. Among more than one hundred 2D/3D reviewed LuRs, eighteen 3D LuRs are inventoried and discussed thoroughly. For each of these LuRs, the research work identifies and proposes the required information (as level of information need) by considering both geometries and semantics to combine modelled LuRs with other sources of information (e.g., BIM, CityGML, and planning maps). Finally, the thesis proposes the level of information need considering requirements to verify 3D LuRs automatically for detecting potential conflicts using analytical rules (e.g., clash detection). The proof of feasibility is a web-based prototype based on a case study located in the City of Melbourne (where planning activities are under the control of authorities in the state of Victoria, Australia) focusing on the planning permit process. The following 3D LuRs were modelled and verified: 1) building height limits, 2) energy efficiency protection, 3) overshadowing open space, 4) noise impacts, 5) overlooking, 6) side and rear setbacks, 7) street setbacks (side and front), and 8) flooding limits

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research

Definição e representação da corpora geoespacial para mapas indoor 3D a partir de nuvem de pontos

Orientador: Prof. Dr. Daniel Rodrigues dos SantosTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 15/12/2022Inclui referências: p. 149-156Resumo: O termo corpora geoespaciais é definido como um conjunto de dados geoespaciais, sistematizados segundo determinados critérios de maneira que sejam representativos do espaço que se deseja mapear, e pode, a partir de concepções probabilísticas e combinatórias analisar a colocabilidade de um dado geoespacial em decorrência da descrição do ambiente de interesse, buscando revelar respostas a partir de observações estatísticas e identificação de padrões de uso para uma coleção de amostras digitais, fazendo parte da Programação da Linguagem Natural, subárea do Aprendizado de Máquina. No contexto tridimensional, corpora geoespaciais 3D podem ser formulados por conjuntos de nuvens de pontos LiDAR e apresentam alto custo computacional para armazenamento, manipulação e visualização. Além disso, não é um conjunto de dados estruturado e que apresente semântica. Para estruturar e extrair conhecimento formalizado destes corpora geoespaciais 3D é proposto um método de generalização cartográfica 3D usando aprendizado de máquina. A metodologia é baseada na Hipótese da Naturalidade aplicada sobre corpora geoespaciais 3D e na parcimônia da descrição geométrica de ambientes indoor. O método é dividido em 4 etapas. Primeiramente, a partir da análise de autossimilaridade estatística de corpora geoespaciais 3D foram propostos operadores de simplificação. Segundo, foram definidos aspectos semânticos dos corpora analisados para extração de corpora 3D especializados de elementos construtivos empregando uma estratégia de aprendizado de máquina. A terceira etapa consistiu na agregação e simplificação dos corpora conforme feições planas usando o algoritmo RANSAC. Finalmente, é aplicado uma estratégia de agregação de superfícies planas em subespaços, baseada na parcimônia da descrição. Os experimentos foram realizados em seis conjuntos de dados provenientes de um sistema LiDAR terrestre, no modo estático. Os resultados obtidos demonstraram que o aprendizado de máquina e a parcimônia da descrição, explorando a Hipótese da Naturalidade e o contexto da Linguagem Natural, auxiliaram na definição e representação estruturada de corpora geoespaciais 3D para ambientes indoor. Os operadores de simplificação reduziram a massividade do conjunto de pontos em 86%. Enquanto a sua aplicação associada ao processo de agregação estruturou e reduziu a massa de dados em 94%.Abstract: The term geospatial corpora is defined as a set of geospatial data, systematized according to certain criteria so that they are representative of the space to be mapped, and can, based on probabilistic and combinatorial concepts, analyze the collocation of a geospatial data in the occurrence of the description of the environment of interest, seeking to reveal answers from statistical observations and identification of usage patterns for a collection of fingerprints, as part of Natural Language Programming, subarea of Machine Learning. In the three-dimensional context, 3D geospatial corpora can be formulated by sets of LiDAR point clouds and present a high computational cost for storage, manipulation and visualization. Furthermore, it is not a structured and semantic dataset. To address this problem, a method for 3D cartographic generalization of LiDAR points clouds using deep learning is proposed. The proposed method is based on the naturalness hypothesis centered on LiDAR point clouds and parsimony of the geometric description of indoor environments. The contribution of the proposed four-fold. First, a set of operators for simplification tasks is defined from a LiDAR point clouds correlation statistical technique. Second, a deep learning technique is used for LiDAR point clouds semantic segmentation. Third, the RANSAC algorithm is executed to fit planar surfaces. Finally, a parsimony descriptor-based aggregation strategy is investigated. The proposed method was tested on six sets of LiDAR point clouds. The experimental results have demonstrated that by exploring the naturalness hypothesis centered on LiDAR point clouds, indoor environment modeling was successfully obtained for LoD2. The operators for simplification reduced the high volume of the LiDAR data by around 86%. On the other hand, the aggregation task showed that the LiDAR data can be reduced by around 94%