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Time-dependent analysis scenarios such as heat, wind or flood analysis in cities and in landscapes need a correct and consistent modelling of geometry and topology over time. However, hitherto efficient time-dependent geometry models and topological analysis based on a mathematically sound theory were neglected when modelling objects in the built and natural environment. This is surprising as incorrect topological relationships over time such as not fitting neighbourhoods of surfaces or solids inevitably lead to wrong analysis results. In this paper we propose the combination of a spatio-temporal geometry model together with a topological schema to provide accessible and consistent objects over time. Where an efficient spatio-temporal geometry model reduces redundant geometric data and enables spatio-temporal queries, an efficient topological model minimizes the number of relations as far as possible and enables robust topological queries. The geometry model uses the concepts of point tubes, delta storage as well as net components and pre- and post-objects to enable the change of geometry and topology over time for natural structures, e.g., digital terrain models (DTM). Geometry here are the boundary and interior coordinates of the objects whereas topology here is interpreted in a wider sense than only focusing on geometrically induced topology to maintain topological consistency by the management of incidence relations. In addition, the topological schema introduces three basic bidirectional relation types to manage aggregations, abstractions and incidences in order to provide a general abstract topological schema for the management of complex intra- and inter-related spatio-temporal objects to enable the modelling of consistent complex topology over time. Finally, a conclusion is given highlighting the applicability of the approach and future research.

During the last decades satellite remote sensing has become an emerging technology producing big data for various application fields every day. However, data quality checking as well as the long-time management of data and models are still issues to be improved. They are indispensable to guarantee smooth data integration and the reproducibility of data analysis such as carried out by machine learning models. In this paper we clarify the emerging need of improving data quality and the management of data and models in a geospatial database management system before and during data analysis. In different use cases various processes of data preparation and quality checking, integration of data across different scales and references systems, efficient data and model management, and advanced data analysis are presented in detail. Motivated by these use cases we then discuss emerging research questions concerning data preparation and data quality checking, data management, model management and data integration. Finally conclusions drawn from the paper are presented and an outlook on future research work is given.

The monitoring of spatio-temporal phenomena such as the movement of glaciers or volcanos produces huge amounts of data. Especially the handling of time as a fourth dimension usually requires terabytes of storage space. Therefore, an appropriate data management model for an efficient handling of such data is required and a key concept for any 3D/4D GIS. Within this paper, a comprehensive data management model is presented that optimizes the handling and storage of spatio-temporal data. The concept of time-dependent Net Components is introduced to handle regions that are monitored and modelled in individual time steps. Combined with the concept of Point Tubes the resulting data management model is used to manage spatio-temporal data based on d-simplicial complexes with dimensions d ∈ 0,1,2,3. It is shown that the model is capable of handling various kinds of spatio-temporal applications. These include the proper handling of variable temporal discretizations of partial regions within a 4D model. The storage requirements are reduced and spatial as well as spatio-temporal operations are accelerated significantly. Critical issues such as the preservation of the net topology between time steps and the handling of boundary regions of adjacent net components can be solved.

The European COPERNICUS program provides an unprecedented breakthrough in the broad use and application of satellite remote sensing data. Maintained on a sustainable basis, the COPERNICUS system is operated on a free-and-open data policy. Its guaranteed availability in the long term attracts a broader community to remote sensing applications. In general, the increasing amount of satellite remote sensing data opens the door to the diverse and advanced analysis of this data for earth system science.However, the preparation of the data for dedicated processing is still inefficient as it requires time-consuming operator interaction based on advanced technical skills. Thus, the involved scientists have to spend significant parts of the available project budget rather on data preparation than on science. In addition, the analysis of the rich content of the remote sensing data requires new concepts for better extraction of promising structures and signals as an effective basis for further analysis.In this paper we propose approaches to improve the preparation of satellite remote sensing data by a geo-database. Thus the time needed and the errors possibly introduced by human interaction are minimized. In addition, it is recommended to improve data quality and the analysis of the data by incorporating Artificial Intelligence methods. A use case for data preparation and analysis is presented for earth surface deformation analysis in the Upper Rhine Valley, Germany, based on Persistent Scatterer Interferometric Synthetic Aperture Radar data. Finally, we give an outlook on our future research.

A significant advantage of OpenStreetMap data is its up-to-dateness. However, for rural and city planning, it is also of importance to access historical data and to compare the changes between new and old versions of the same area. This paper first introduces into a differentiated classification of changes on OpenStreetMap data sets. Then a methodology for an automated database-supported analysis of changes is presented. Beyond the information already provided from the OpenStreetMap server, we present a more detailed analysis with derived information. Based on this approach it is possible to identify objects with attributive or geometric changes and to find out how they exactly differ from their previous versions. The analysis shows in which regions mappers were active during a certain time interval. Furthermore, a time based approach based on various parameters to determine the quality of the data is presented. It provides a guideline of data quality and works without any reference data. Therefore, an indication about the development of OpenStreetMap in terms of completeness and correctness of the data in different regions is given. Finally, a conclusion and an outlook on open research questions are presented.

Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

The use of intelligent technologies within 3D geospatial data analysis and management will decidedly open the door towards efficiency, cost transparency, and on-time schedules in planning processes. Furthermore, the mission of smart cities as a future option of urban development can lead to an environment that provides high-quality life along stable structures. However, neither geospatial information systems nor building information modelling systems seem to be well prepared for this new development. After a review of current approaches and a discussion of their limitations we present our approach on the way to an intelligent platform for the management and analysis of big 3D geospatial data focusing on infrastructure projects such as metro or railway tracks planning. three challenges are presented focusing on the management of big geospatial data with existing geo-database management systems, the integration of heterogeneous data, and the 3D visualization for database query formulation and query results. The approach for the development of a platform for big geospatial data analysis is discussed. Finally, we give an outlook on our future research supporting intelligent 3D city applications in the United Arab Emirates.

Advanced analysis of spatio-temporal data is improving our knowledge about natural phenomena such as landslides and volcanic activity. Furthermore, it can contribute to a deeper understanding of the built environment by predicting city development. However, the data produced by the modelling of these observations can easily rise to terra bytes of 4D (3D space plus time) data. Also, the repeatability of such analysis is essential. Thus, repeatable and reliable access to big spatio-temporal data, models and simulation results should be guaranteed. However, today´s Geographic Information Systems (GIS) are not prepared to meet these requirements. Therefore, we present a comprehensive spatio-temporal data model for GIS and geodatabase management systems that can be used as the “heart” of 4D GIS. The comprehensive model enables a flexible management of time-series-based geometric and topological data. It cares of storage reduction and computational efficiency when storing, retrieving, and processing surface- and volume-based data in 3D space plus time. Furthermore, the model can handle geometry and topology changes of 4D data, i.e., mesh changes of triangulated surfaces and tetrahedral volumes over time. The model also allows the attachment of detailed semantic information to geometric and topological entities. The various concepts, implementation and the benefits of our comprehensive model are presented. Finally, conclusions are drawn from the approach and an outlook is given on future research including demanding applications based on multiscale digital elevation models.

Background Evidence shows that online interventions could prevent depression. However, to improve the effectiveness of preventive online interventions in individuals with subthreshold depression, it is worthwhile to study factors influencing intervention outcomes. Outcome expectancy has been shown to predict treatment outcomes in psychotherapy for depression. However, little is known about whether this also applies to depression prevention. The aim of this study was to investigate the role of participants' outcome expectancy in an online depression prevention intervention. Methods A secondary data analysis was conducted using data from two randomised‐controlled trials (N = 304). Multilevel modelling was used to explore the effect of outcome expectancy on depressive symptoms and close‐to‐symptom‐free status postintervention (6–7 weeks) and at follow‐up (3–6 months). In a subsample (n = 102), Cox regression was applied to assess the effect on depression onset within 12 months. Explorative analyses included baseline characteristics as possible moderators. Outcome expectancy did not predict posttreatment outcomes or the onset of depression. Results Small effects were observed at follow‐up for depressive symptoms (β = −.39, 95% confidence interval [CI]: [−0.75, −0.03], p = .032, padjusted = .130) and close‐to‐symptom‐free status (relative risk = 1.06, 95% CI: [1.01, 1.11], p = .013, padjusted = 0.064), but statistical significance was not maintained when controlling for multiple testing. Moderator analyses indicated that expectancy could be more influential for females and individuals with higher initial symptom severity. Conclusion More thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention. Patient or Public Contribution This secondary analysis did not involve patients, service users, care‐givers, people with lived experience or members of the public. However, the findings incorporate the expectations of participants using the preventive online intervention, and these exploratory findings may inform the future involvement of participants in the design of indicated depression prevention interventions for adults. Clinical Trial Registration Original studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.

Digital Terrain Models (DTMs) are still one of the most emerging products derived from remote sensing technologies such as LiDAR. A DTM usually is defined as the current digital representation of the earth´s surface. However, due to natural events and human influences the earth's surface is constantly changing. Therefore, the ability to analyse DTM data over time allows to understand and quantify changes, which are crucial for environmental monitoring, disaster management and urban planning. In this paper we introduce a GeoDBMS-based approach to manage, sustainably provide, analyse and visualize spatio-temporal DTM data. First, we review current approaches for the management and temporal modelling of DTM data. Then we present an event-based time-stamping spatio-temporal data model that meets special requirements for the management and analysis of DTMs to monitor spatial changes within the DTM in time. Based on this model, we present methods for the management and analysis of spatio-temporal DTMs. Thereby the model is extended by an appropriate server-infrastructure and a graphical user interface that enables to query, analyze, visualize and export time series of DTMs. Data consistency and accuracy for DTMs are also considered. A use case is conducted based on spatio-temporal DTM datasets of South-West Germany, demonstrating the suitability of the approach. Furthermore, geodetic applications and new DTM research directions are shown. Finally, conclusions are drawn from the paper and an outlook is presented describing our future research based on new use cases, including the analysis of DTMs and Digital Surface Models (DSMs) in the United Arab Emirates.