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In 2012, the Open Geospatial Consortium published GeoSPARQL defining “an RDF/OWL ontology for [spatial] information”, “SPARQL extension functions” for performing spatial operations on RDF data and “RIF rules” defining entailments to be drawn from graph pattern matching. In the 8+ years since its publication, GeoSPARQL has become the most important spatial Semantic Web standard, as judged by references to it in other Semantic Web standards and its wide use for Semantic Web data. An update to GeoSPARQL was proposed in 2019 to deliver a version 1.1 with a charter to: handle outstanding change requests and source new ones from the user community and to “better present” the standard, that is to better link all the standard’s parts and better document and exemplify elements. Expected updates included new geometry representations, alignments to other ontologies, handling of new spatial referencing systems, and new artifact presentation. This paper describes motivating change requests and actual resultant updates in the candidate version 1.1 of the standard alongside reference implementations and usage examples. We also describe the theory behind particular updates, initial implementations of many parts of the standard, and our expectations for GeoSPARQL 1.1’s use.

The study of global phenomena requires the combination of a considerable amount of data coming from different sources, acquired by different observation platforms and managed by institutions working in different scientific fields. Merging this data to provide extensive and complete data sets to monitor the long-term, global changes of our oceans is a major challenge. The data acquisition and data archival procedures usually vary significantly depending on the acquisition platform. This lack of standardization ultimately leads to information silos, preventing the data to be effectively shared across different scientific communities. In the past years, important steps have been taken in order to improve both standardization and interoperability, such as the Open Geospatial Consortium’s Sensor Web Enablement (SWE) framework. Within this framework, standardized models and interfaces to archive, access and visualize the data from heterogeneous sensor resources have been proposed. However, due to the wide variety of software and hardware architectures presented by marine sensors and marine observation platforms, there is still a lack of uniform procedures to integrate sensors into existing SWE-based data infrastructures. In this work, a framework aimed to enable sensor plug and play integration into existing SWE-based data infrastructures is presented. First, an analysis of the operations required to automatically identify, configure and operate a sensor are analysed. Then, the metadata required for these operations is structured in a standard way. Afterwards, a modular, plug and play, SWE-based acquisition chain is proposed. Finally different use cases for this framework are presented.

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques.

This paper explains the development of a 3D city model-based Public Participation Platform as a prototype and its implementation in a real-world public participation process to redevelop the Weilimdorf area of Stuttgart city. Alongside conducting Weilimdorf’s public participation process, the goal of the mentioned public participation platform is to research citizens’ acceptance of such tools. The usage of digital tools has become more critical for participation processes. The need for social distancing expedites this change, particularly during the pandemic. Previous research frequently focuses on 2D platforms and smaller sample sizes but nevertheless shows the importance of such tools. However, with current developments in geospatial and web streaming technologies, it has become easier and faster to visualize large-scale 3D city models over the web. In this research, these technologies were used by the citizens of the Weilimdorf area to evaluate the usability of the platform and collect their feedback. The result shows that such a digital public participation platform is a valuable supplement to traditional in-person public participation methods.

The concept and implementation of Smart Cities is an important approach to improve decision making as well as quality of life of the growing urban population. An essential part of this is the presentation of data from different sources within a digital city model. Wind flow at building scale has a strong impact on many health and energy issues in a city. For the analysis of urban wind, Computational Fluid Dynamics (CFD) has become an established tool, but requires specialist knowledge to prepare the geometric input during a time-consuming process. Results are available only as predefined selections of pictures or videos. In this article, a continuous, semi-automated workflow is presented, which ❶ speeds-up the preparation of CFD simulation models using a largely automated geometry optimization; and ❷ enables web-based interactive exploration of urban wind simulations to a large and diverse audience, including experts and layman. Results are evaluated based on a case study using a part of a district in Stuttgart in terms of: ➀ time saving of the CFD model preparation workflow (85% faster than the manual method), ➁ response time measurements of different data formats within the Smart City platform (3D Tiles loaded 30% faster than geoJSON using the same data representations) and ➂ protocols (3DPS provided much higher flexibility than static and 3D container API), as well as ➃ subjective user experience analysis of various visualization schemes of urban wind. Time saving for the model optimization may, however, vary depending on the data quality and the extent of the study area.

As the world has more urbanized, cities need to assess and manage their building energy performances in order to achieve energy-reduction goals. The urban digital twins (UDT) offer promising solutions to this demand by providing valuable insights with qualitative and quantitative information about the building environment. The urban building energy data is not only measured from the equipped sensor devices but can also be simulated based on the analysis software. In this research, we aim to explore the development of the spatial data infrastructure (SDI) for managing building energy in the UDT application by employing the Open Geospatial Consortium (OGC) standards which increases the data usability and efficiency. In our concept SDI, the big data in the UDT application is managed with the OGC specifications as follows: OGC SensorThings API (STA) for data with Spatio-temporal characteristics, OGC API 3D GeoVolumes for 3D geospatial content delivery, OGC CityGML for virtual 3D city models, OGC API Features, Web Feature Service (WFS), and Web Map Service (WMS) for other 2D geospatial contents. This concept enables broad interoperability between multiple data layers and client applications. As a proof of concept, we developed the UDT application called with a highly visual and intuitive user interface using the proposed SDI concept as a part of the EnSysLE project in the study area of three regions in Germany: Ludwigsburg, Dithmarschen, and Ilm-Kreis. The proposed concept can be expanded to other UDT domains and on a larger scale in future work.

The effective tracking and analysis of moving objects within urban environments presents a complex challenge that necessitates robust geospatial data integration. Open Geospatial Consortium (OGC) APIs offer standardized approaches to managing dynamic geospatial information. This paper presents a case study of real-time moving object tracking including buses and trains in the city of Munakata, Japan, utilizing two prominent OGC APIs: OGC API Moving Features and OGC SensorThings API. The study explores the implementation of both APIs, examining their strengths and limitations in handling real-time location updates and associated sensor data generated by moving buses. The research provides insights into the practical suitability of each API model for dynamic object tracking, offering valuable guidance for practitioners seeking to optimize geospatial data integration within smart cities and intelligent transportation systems.

The world-wide sensor web has become a very useful technique for monitoring the physical world at spatial and temporal scales that were previously impossible. Yet we believe that the full potential of sensor web has thus far not been revealed. In order to harvest the world-wide sensor web’s full potential, a geospatial cyberinfrastructure is needed to store, process, and deliver large amount of sensor data collected worldwide. In this paper, we first define the issue of the sensor web long tail followed by our view of the world-wide sensor web architecture. Then, we introduce the Geospatial Cyberinfrastructure for Environmental Sensing (GeoCENS) architecture and explain each of its components. Finally, with demonstration of three real-world powered-by-GeoCENS sensor web applications, we believe that the GeoCENS architecture can successfully address the sensor web long tail issue and consequently realize the world-wide sensor web vision.

The recent advancement in Information & Communication Technology (ICT) is seen as a critical enabler to design intelligent smart cities targeting different domains. One such domain is modes of transport in a city. Currently, various cities around the world are envisioning innovative ways to reduce emissions in the cities by increasing physically active mobility. However, there is still limited information about the safety of cyclists and pedestrians within city limits. To address this, we develop a 3D web-based safe routing tool called Vision Zero. Our concept prototype used Augsburg city, Germany, as a case study. The implementation is based on open-source tools. In the back-end, the OGC 3D Portrayal Service standard helps to deliver and integrate various 2D and 3D geospatial contents on a web-based client using CesiumJS. The OGC SensorThings API (STAPI) standard is used to manage historical and real-time open road-incident data from the Federal Statistical Office of Germany. The navigation system is built up based on the routing engine pgRouting, which calculates the safest route based on the mentioned STAPI server and the road-network dataset from OpenStreetMap.

To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS method includes both parallel architecture and a mechanism for adjusting the computational resources—the parallel geocomputing mechanism of the P-AaaS method used to execute a geospatial service and the execution algorithm of the P-AaaS based geospatial service chain, respectively. The P-AaaS based method has the following merits: (1) it inherits the advantages of the AaaS-based method (i.e., avoiding transfer of large volumes of remote sensing data or raster terrain data, agent migration, and intelligent conversion into services to improve domain expert collaboration); (2) it optimizes the low performance and the concurrent geoprocessing capability of the AaaS-based method, which is critical for special applications (e.g., highly concurrent applications and emergency response applications); and (3) it adjusts the computing resources dynamically according to the number and the performance requirements of concurrent requests, which allows the geospatial service chain to support a large number of concurrent requests by scaling up the cloud-based clusters in use and optimizes computing resources and costs by reducing the number of virtual machines (VMs) when the number of requests decreases.