Raster Time Series: Learning and Processing

As the amount of remote sensing data is increasing at a high rate, due to great improvements in sensor technology, efficient processing capabilities are of utmost importance. Remote sensing data from satellites is crucial in many scientific domains, like biodiversity and climate research. Because weather and climate are of particular interest for almost all living organisms on earth, the efficient classification of clouds is one of the most important problems. Geostationary satellites such as Meteosat Second Generation (MSG) offer the only possibility to generate long-term cloud data sets with high spatial and temporal resolution. This work, therefore, addresses research problems on efficient and parallel processing of MSG data to enable new applications and insights. First, we address the lack of a suitable processing chain to generate a long-term Fog and Low Stratus (FLS) time series. We present an efficient MSG data processing chain that processes multiple tasks simultaneously, and raster data in parallel using the Open Computing Language (OpenCL). The processing chain delivers a uniform FLS classification that combines day and night approaches in a single method. As a result, it is possible to calculate a year of FLS rasters quite easy. The second topic presents the application of Convolutional Neural Networks (CNN) for cloud classification. Conventional approaches to cloud detection often only classify single pixels and ignore the fact that clouds are highly dynamic and spatially continuous entities. Therefore, we propose a new method based on deep learning. Using a CNN image segmentation architecture, the presented Cloud Segmentation CNN (CS-CNN) classifies all pixels of a scene simultaneously. We show that CS-CNN is capable of processing multispectral satellite data to identify continuous phenomena such as highly dynamic clouds. The proposed approach provides excellent results on MSG satellite data in terms of quality, robustness, and runtime, in comparison to Random Forest (RF), another widely used machine learning method. Finally, we present the processing of raster time series with a system for Visualization, Transformation, and Analysis (VAT) of spatio-temporal data. It enables data-driven research with explorative workflows and uses time as an integral dimension. The combination of various raster and vector data time series enables new applications and insights. We present an application that combines weather information and aircraft trajectories to identify patterns in bad weather situations

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Efficient Point Clustering for Visualization

The visualization of large spatial point data sets constitutes a problem with respect to runtime and quality. A visualization of raw data often leads to occlusion and clutter and thus a loss of information. Furthermore, particularly mobile devices have problems in displaying millions of data items. Often, thinning via sampling is not the optimal choice because users want to see distributional patterns, cardinalities and outliers. In particular for visual analytics, an aggregation of this type of data is very valuable for providing an interactive user experience. This thesis defines the problem of visual point clustering that leads to proportional circle maps. It furthermore introduces a set of quality measures that assess different aspects of resulting circle representations. The Circle Merging Quadtree constitutes a novel and efficient method to produce visual point clusterings via aggregation. It is able to outperform comparable methods in terms of runtime and also by evaluating it with the aforementioned quality measures. Moreover, the introduction of a preprocessing step leads to further substantial performance improvements and a guaranteed stability of the Circle Merging Quadtree. This thesis furthermore addresses the incorporation of miscellaneous attributes into the aggregation. It discusses means to provide statistical values for numerical and textual attributes that are suitable for side-views such as plots and data tables. The incorporation of multiple data sets or data sets that contain class attributes poses another problem for aggregation and visualization. This thesis provides methods for extending the Circle Merging Quadtree to output pie chart maps or maps that contain circle packings. For the latter variant, this thesis provides results of a user study that investigates the methods and the introduced quality criteria. In the context of providing methods for interactive data visualization, this thesis finally presents the VAT System, where VAT stands for visualization, analysis and transformation. This system constitutes an exploratory geographical information system that implements principles of visual analytics for working with spatio-temporal data. This thesis details on the user interface concept for facilitating exploratory analysis and provides the results of two user studies that assess the approach

A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB

Adaptive main-memory indexing for high-performance point-polygon joins

Connected mobility applications rely heavily on geospatial joins that associate point data, such as locations of Uber cars, to static polygonal regions, such as city neighborhoods. These joins typically involve expensive geometric computations, which makes it hard to provide an interactive user experience. In this paper, we propose an adaptive polygon index that leverages true hit fltering to avoid expensive geometric computations in most cases. In particular, our approach closely approximates polygons by combining quadtrees with true hit filtering, and stores these approximations in a query-effcient radix tree. Based on this index, we introduce two geospatial join algorithms: an approximate one that guarantees a user-defined precision, and an exact one that adapts to the expected point distribution. In summary, our technique outperforms existing CPU-based joins by up to two orders of magnitude and is competitive with state-of-the-art GPU implementations

Fachlich erweiterbare 3D-Stadtmodelle – Management, Visualisierung und Interaktion

Domain-extendable semantic 3D city models are complex mappings and inventories of the urban environment which can be utilized as an integrative information backbone to facilitate a range of application fields like urban planning, environmental simulations, disaster management, and energy assessment. Today, more and more countries and cities worldwide are creating their own 3D city models based on the CityGML specification which is an international standard issued by the Open Geospatial Consortium (OGC) to provide an open data model and XML-based format for describing the relevant urban objects with regards to their 3D geometry, topology, semantics, and appearance. It especially provides a flexible and systematic extension mechanism called “Application Domain Extension (ADE)” which allows third parties to dynamically extend the existing CityGML definitions with additional information models from different application domains for representing the extended or newly introduced geographic object types within a common framework. However, due to the consequent large size and high model complexity, the practical utilization of country-wide CityGML datasets has posed a tremendous challenge regarding the setup of an extensive application system to support the efficient data storage, analysis, management, interaction, and visualization. These requirements have been partly solved by the existing free 3D geo-database solution called ‘3D City Database (3DCityDB)’ which offers a rich set of functionalities for dealing with standard CityGML data models, but lacked the support for CityGML ADEs. The key motivation of this thesis is to develop a reliable approach for extending the existing database solution to support the efficient management, visualization, and interaction of large geospatial data elements of arbitrary CityGML ADEs. Emphasis is first placed on answering the question of how to dynamically extend the relational database schema by parsing and interpreting the XML schema files of the ADE and dynamically create new database tables accordingly. Based on a comprehensive survey of the related work, a new graph-based framework has been proposed which uses typed and attributed graphs for semantically representing the object-oriented data models of CityGML ADEs and utilizes graph transformation systems to automatically generate compact table structures extending the 3DCityDB. The transformation process is performed by applying a series of fine-grained graph transformation rules which allow users to declaratively describe the complex mapping rules including the optimization concepts that are employed in the development of the 3DCityDB database schema. The second major contribution of this thesis is the development of a new multi-level system which can serve as a complete and integrative platform for facilitating the various analysis, simulation, and modification operations on the complex-structured 3D city models based on CityGML and 3DCityDB. It introduces an additional application level based on a so-called ‘app-concept’ that allows for constructing a light-weight web application to reach a good balance between the high data model complexity and the specific application requirements of the end users. Each application can be easily built on top of a developed 3D web client whose functionalities go beyond the efficient 3D geo-visualization and interactive exploration, and also allows for performing collaborative modifications and analysis of 3D city models by taking advantage of the Cloud Computing technology. This multi-level system along with the extended 3DCityDB have been successfully utilized and evaluated by many practical projects.Fachlich erweiterbare semantische 3D-Stadtmodelle sind komplexe Abbildungen und Datenbestände der städtischen Umgebung, die als ein integratives Informationsrückgrat genutzt werden können, um eine Reihe von Anwendungsfeldern wie z. B. Stadtplanung, Umweltsimulationen, Katastrophenmanagement und Energiebewertung zu ermöglichen. Heute schaffen immer mehr Länder und Städte weltweit ihre eigenen 3D-Stadtmodelle auf Basis des internationalen Standards CityGML des Open Geospatial Consortium (OGC), um ein offenes Datenmodell und ein XML-basiertes Format zur Beschreibung der relevanten Stadtobjekte in Bezug auf ihre 3D-Geometrien, Topologien, Semantik und Erscheinungen zur Verfügung zu stellen. Es bietet insbesondere einen flexiblen und systematischen Erweiterungsmechanismus namens „Application Domain Extension“ (ADE), der es Dritten ermöglicht, die bestehenden CityGML-Definitionen mit zusätzlichen Informationsmodellen aus verschiedenen Anwendungsdomänen dynamisch zu erweitern, um die erweiterten oder neu eingeführten Stadtobjekt-Typen innerhalb eines gemeinsamen Framework zu repräsentieren. Aufgrund der konsequent großen Datenmenge und hohen Modellkomplexität bei der praktischen Nutzung der landesweiten CityGML-Datensätze wurden jedoch enorme Anforderungen an den Aufbau eines umfangreichen Anwendungssystems zur Unterstützung der effizienten Speicherung, Analyse, Verwaltung, Interaktion und Visualisierung der Daten gestellt. Die bestehende kostenlose 3D-Geodatenbank-Lösung „3D City Database“ (3DCityDB) entsprach bereits teilweise diesen Anforderungen, indem sie zwar eine umfangreiche Funktionalität für den Umgang mit den Standard-CityGML-Datenmodellen, jedoch keine Unterstützung für CityGML-ADEs bietet. Die Schlüsselmotivation für diese Arbeit ist es, einen zuverlässigen Ansatz zur Erweiterung der bestehenden Datenbanklösung zu entwickeln, um das effiziente Management, die Visualisierung und Interaktion großer Datensätze beliebiger CityGML-ADEs zu unterstützen. Der Schwerpunkt liegt zunächst auf der Beantwortung der Schlüsselfrage, wie man das relationale Datenbankschema dynamisch erweitern kann, indem die XML-Schemadateien der ADE analysiert und interpretiert und anschließend dem entsprechende neue Datenbanktabellen erzeugt werden. Auf Grundlage einer umfassenden Studie verwandter Arbeiten wurde ein neues graphbasiertes Framework entwickelt, das die typisierten und attributierten Graphen zur semantischen Darstellung der objektorientierten Datenmodelle von CityGML-ADEs verwendet und anschließend Graphersetzungssysteme nutzt, um eine kompakte Tabellenstruktur zur Erweiterung der 3DCityDB zu generieren. Der Transformationsprozess wird durch die Anwendung einer Reihe feingranularer Graphersetzungsregeln durchgeführt, die es Benutzern ermöglicht, die komplexen Mapping-Regeln einschließlich der Optimierungskonzepte aus der Entwicklung des 3DCityDB-Datenbankschemas deklarativ zu formalisieren. Der zweite wesentliche Beitrag dieser Arbeit ist die Entwicklung eines neuen mehrstufigen Systemkonzepts, das auf CityGML und 3DCityDB basiert und gleichzeitig als eine komplette und integrative Plattform zur Erleichterung der Analyse, Simulationen und Modifikationen der komplex strukturierten 3D-Stadtmodelle dienen kann. Das Systemkonzept enthält eine zusätzliche Anwendungsebene, die auf einem sogenannten „App-Konzept“ basiert, das es ermöglicht, eine leichtgewichtige Applikation bereitzustellen, die eine gute Balance zwischen der hohen Modellkomplexität und den spezifischen Anwendungsanforderungen der Endbenutzer erreicht. Jede Applikation lässt sich ganz einfach mittels eines bereits entwickelten 3D-Webclients aufbauen, dessen Funktionalitäten über die effiziente 3D-Geo-Visualisierung und interaktive Exploration hinausgehen und auch die Durchführung kollaborativer Modifikationen und Analysen von 3D-Stadtmodellen mit Hilfe von der Cloud-Computing-Technologie ermöglichen. Dieses mehrstufige System zusammen mit dem erweiterten 3DCityDB wurde erfolgreich in vielen praktischen Projekten genutzt und bewertet