More and more applications are becoming available that rely on the OGC Sensor Web Enablement (SWE) framework for integrating sensors and their measurements into spatial data infrastructures. While functionality allowing the access to sensor data, the tasking of sensors and the alerting based on measured values is already available through the OGC SWE architecture, an approach for discovering sensors, sensor data, and Sensor Web services is missing. Based on requirements derived from different research and application contexts this book closes this gap by offering a conceptual framework for discovery within the Sensor Web. The results described in this book comprise a concept and implementation of a Sensor Web discovery service, the design of metadata models for describing resources in the Sensor Web as well as an approach how to automatically collect and process metadata from Sensor Web services. In addition, a mechanism is introduced how the Sensor Web world and the OGC Catalog, as a core element of spatial data infrastructures, can be coupled. Finally, this book offers a concept for integrating the semantics of phenomena observed by sensors (the observed properties) into the discovery process.In summary, this book offers valuable guidance to all who want to publish sensor data or want to enhance existing SDIs with sensor data.

Spatial Data Infrastructures have evolved from concepts into reality in the last decade. However, they are still focused on data retrieval and visualization. To proceed in the direction of a true Spatial Information Infrastructure, it is evident to process geodata to geoinformation. This book goes beyond chaining freely available Geoprocessing Web Services and assumes that not all services are freely available. Specific functionalities of these distributed Geoprocessing Web Services might be secured and need to be accessed in an on-demand fashion. A concept is presented which allows the dynamic access to and chaining of secured Geoprocessing Web Services without a priori established rights or direct trust relationships in contradiction to the classical Role-Based Access Control scheme.The three different workflow patterns—Transparent, Translucent and Opaque—are analyzed on the basis of a real-world scenario. A static model, a trust model and a dynamic model are developed for each of the three workflow patterns.In general, the concepts presented in this thesis can be seen as enablers for mapping full business processes in Spatial Information Infrastructures across enterprise borders. This aspect forms an important step towards commercial applications in Spatial Information Infrastructures.

Real-time monitoring of urban dynamics, which is widely unexplored, has recently received a lot of attention due to the fast rise of inexpensive pervasive sensor technologies. Following the comprehensive vision of a “digital skin for planet Earth” by Neil Gross (1999), it can be presumed that geo-sensor web deployments will experience a considerable boost within the coming years, as pervasive sensing has recently become feasible and affordable. This enriches knowledge about our environment with uncharted real-time information layers.This book focuses on pervasive sensing in urban environments, which poses very particular challenges – as well technical and technological ones, as socio-political and privacy-related ones. The Live Geography approach presented in this book seeks to tackle these challenges with an open sensing infrastructure for urban monitoring applications. The system makes extensive use of open (geospatial) standards throughout the entire process chain – from sensor fusion to data analysis, Complex Event Processing (CEP), alerting, and finally visualisation.The thesis discusses the implemented technical infrastructure as well as how the methodology can potentially influence the city and its inhabitants by “making the abstract real”. In other words, the thesis illustrates how pervasive monitoring infrastructures can change urban social interactions and people’s short-term behaviour, and which issues are related to establishing such systems.

Because of the increasing use of decentrally held data and networked services, detailed knowledge about the existing data (for example its origin, structure, formats, quality and reference applications) becomes more and more important. The availability of such metadata and the evaluation of the fitness for use based on these metadata are vital. This book intents to contribute to the development of meaningful and machine-interpretable quality descriptions of geographical information. It focuses on spatio- temporal semantic integrity constraints. Three major contributions are elaborated: (i) a detailed categorisation of semantic integrity constraints, (ii) a framework for the formal definition of the constraints and (iii) a reasoning methodology for the detection of conflicting and redundant constraints. Possible application areas are quality assurance of geodata, geodata integration and harmonisation, data modelling and ontology engineering, semantic similarity measurement and usability evaluation.Awarded with the Dissertation Price 2010 for excellent dissertations by the Association of Friends of the University of the Bundeswehr Munich (Freundeskreis der Universität der Bundeswehr München e.V.)

This work is about human beings and Geographic Information Systems (GIS). More specifically, it introduces methods and techniques to foster natural interaction among the two; an interaction that becomes continuously more important as technological improvements bring GIS technology within general public‘s reach. A glaring example concerns, for instance, Volunteered Geographic Information projects that relies on the intervention of volunteers − i.e., not necessarily GIS experts − to build and share freely accessible spatial datasets.According to the current state of the art, there is a fundamental representational gap that, by hindering the coveted natural interaction, prevents non-experts from fully exploiting GIS power: if on the one hand human beings prefer to represent and reason about space in a qualitative manner, on the other hand, GIS mainly resort to quantitative representations. To bridge such a gap, a new typology of queries is presented in this work: Qualitative Spatial Relation Queries (QSRQ) involve spatial predicates defined in mathematical structures known as qualitative calculi. Such predicates provide both a computer-readable definition and a cognitively-adequate semantics to represent and reason about given spatial aspects − e.g., topology or direction. An extensible theoretical and practical framework is presented that allows for enabling QSRQ in GIS. Moreover, a classification of such queries is provided based on their solving complexity and the focus is shifted on the discussion of a new type of spatial access methods − Qualitative Spatial Access Methods (QSAM) − designed for resolving the hardest QSRQ category, termed Qualitative Spatial Configuration Queries. QSAM draw upon a qualitative spatial relation reduction/reconstruction paradigm in order to face the exponential explosion in the amount of information bits introduced by the qualitative formalism. Four different types of QSAM are presented, evaluated, and compared with respect to the space-time performance that they allow for reaching with both synthetic and real spatial datasets.

Extreme events cause drastic transformations in human settlements; as a consequence, descriptions of the struck environment, based on „quantitative“ spatial data collected before the event, becomes suddenly unreliable. On the other hand, time critical actions taken for responding to extreme events require up-to-date spatial information. Traditional methods for spatial data collection are not able to provide updated information rapidly enough, calling for the development of new data collection methods. Reports provided by actors involved in the response operations can be considered as a source of „qualitative“ spatial information. There has been considerable research on spatial representations and analysis based on metric measurements that lead to the development of Geographic Information Systems (GIS). A different field of research focuses on developing representation and reasoning techniques for dealing with qualitative spatial information that describes qualities and relations holding among objects in space abstracting from quantitative representations. Relatively little work exists on finding techniques to combine the different representation and reasoning methodologies. A hybrid reasoning system that combines techniques developed separately for qualitative spatial reasoning and quantitative data analysis is developed in this work. The integrated approach performs different reasoning operations to retrieve new information pieces from a given set. Derived spatial information can be either qualitative or quantitative and complements the given one in supporting tasks related to visualization (e.g., maps and graphics) or querying (e.g., support for decision making). Three main system functionalities are investigated: The first, called quantification, allows for translating qualitative information into quantitative descriptions. The second, called qualification, aims at translating quantitative information into qualitative relations. Finally, the third allows for performing inference operations with information given partly qualitatively and partly quantitatively for boosting the spatial knowledge the system is able to produce.

Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making. In order to infer meaningful information about geographic occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. Here, questions that call for clarification are: How can geographic occurrences be formally modeled with respect to properties observed by sensors? How can the formal model be used to infer knowledge about geographic occurrences from sensor observations? This book delivers a formal model to answer these ontological questions. The model is used to infer information about blizzards from time series produced by a weather station.

Local variations of place names are typically not present in official place name directories. Spatially-aware information retrieval tools or navigation systems rely on these gazetteers to resolve the place names into coordinates. Place names commonly appear in user-generated information on the Web, for example as tags used to describe the content of a picture. Local variations are expected to exist in the long tail of the frequency distribution of terms appearing in user-generated annotations. This thesis explores whether these annotations are a useful source to automatically acquire not officially recognised place names. It presents a methodology analysing point patterns to assess if a term refers to a place. These point patterns represent the spatial and temporal coverage of a term. A cluster analysis further separates point patterns capturing the extent of ambiguous place names. The resulting dictionary of place names can be an invaluable source for any application where users are expected to use place names in search queries.