52°North Spatial Information Research GmbH

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Spatial Data Science

Spatial Data Science

Developing analytical tools to address real world problems

The ever-increasing volume and variety of data available creates a huge potential for answering a wide range of questions. To understand this data and to derive answers, we need analytical tools to model the relationships that emerge from it. Our Spatial Data Science team develops data-driven solutions to real-world problems. Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR) are common themes across several R&D projects. Geoinformation and Spatial Data Science can play a key role in planning and communicating actions to decision makers, stakeholders and the general public.

By exploring and researching analytical tools ranging from linear statistics and the latest multivariate distributions to modern machine learning (ML) and artificial intelligence (AI) approaches, we develop solutions that are appropriate to the problem at hand given the data available. This requires a solid understanding of data and business. We use and contribute to open source tools where possible and encourage Citizen Science.

We are involved in several R&D and PS projects covering a range of topics. The challenge of making Earth Observation time series accessible and providing unified processing and analysis tools has been and remains an engineering task to solve several open questions. The meaningful integration of heterogeneous data sources (from geospatial observations to official statistics) and the adaptation of machine learning (ML) and artificial intelligence (AI) algorithms to the specificities of spatial and spatio-temporal data are central themes of future tasks. A common goal is to develop tools that are of high value to end users. Therefore, a co-design approach often helps to identify the relevant questions within a project and enables us to provide meaningful solutions.

SDI Innovations

SDI Innovations

Solutions for innovative geodata management and processing

A set of projects and activities we call “Spatial Data Infrastructure Innovations” focuses on identifying and realizing innovative solutions for transforming, managing, analyzing and visualizing data from different sources. In close collaboration with our partners and customers, we aim to address the challenges posed by the increasing volume and heterogenity of data, as well as the velocity of data streams. The demand for new and advanced methodologies, technologies, and architectures is strong and we expect it to continue to grow in the near future. We are currently working on:

  • Processing Scalability: identifying and implementing requirements for horizontal and vertical scaling of processing algorithms
  • Workflow Chains and Orchestration: designing and automating complex process workflows
  • Earth Observation Data Processing: handling massive amounts of EO data in an efficient and scalable manner
  • Fit For Purpose Data: pre-processing, ingesting, and delivering raster and vector data to fit the purpose of a specific solution
  • Cloud Environments (PaaS, IaaS, SaaS): leveraging their capabilities in terms of deployment patterns, input data handling, and processing results
  • Efficient Storage Concepts and Formats: organizing vast amounts of data with the right concepts and access patterns, e.g. cloud optimizations
  • Standardization: harmonizing processing interfaces, creating and using interoperable data formats
  • Processing Transparency: strengthening reproducibility, data quality and metadata quality, and process discovery

We address these challenges in a number of R&D and PS projects. Our research partners and customers come from academia and industry and cover a wide range of application domains, such as environmental monitoring, emerging market development, climate change, or disaster management. This diversity enables us to develop new approaches that address the needs of many real-world problems and use cases.

Research Data Infrastructures

Research Data Infrastructures

Enabling the efficient flow, management and visualization of research data

When we talk about Research Data Infrastructures, we are referring to all aspects related to the collection, management, discovery, sharing, and visualization of data that describe the past, present, or future state of the environment. This includes not only data generated by sensors (e.g., measurements of air pollutant concentrations and water levels), but also information collected by humans (e.g., observations of animals or plants) and predictions calculated by models (e.g., weather models). The Research Data Infrastructures team supports our partners and customers along the entire process chain from data acquisition to visualization.

We focus on new approaches and technologies for the development of research data infrastructures and are currently addressing the following challenges:

  • Connecting to sensor hardware: investigating how to efficiently integrate new sensors into (research) data infrastructures, e.g. using IoT technologies such as MQTT
  • Data storage and management: optimizing data models and supporting the provision of metadata to ensure correct interpretation of sensor data
  • Research data management: supporting the management of sensor data as an important element of research data infrastructures
  • Semantic interoperability: contributing to and promoting the use of vocabularies
  • Data availability to users: advancing data access interfaces and encodings
  • Efficient data delivery: enabling push/event-driven communication patterns to ensure timely delivery of data
  • Access control: developing strategies to control access to (dynamic) sensor data sets/streams
  • Data visualization: improving methods for visualizing and exploring sensor data to enable better communication of the collected information
  • Citizen Science: facilitating the establishment of citizen observatory initiatives by providing dedicated reusable building blocks

Our activities also result in active contributions to international standardization efforts. We drive interoperability through our participation in the Open Geospatial Consortium (OGC) and the European INSPIRE framework.

Open source software is an important result of our research activities. In addition to our suite of well-established Sensor Web components (e.g. OGC Sensor Observation Service implementation, our Helgoland API, and the Helgoland Sensor Web viewer), which are used in operational systems, we are regularly publish prototypes that demonstrate and provide an entry point to new technologies. Our work is available via our GitHub repositories.

Publications

Publications

Aguilar, R., Calisto, L., Flacke, J., Akbar, A., & Pfeffer, K. (2021). OGITO, an Open Geospatial Interactive Tool to support collaborative spatial planning with a maptable. Computers, Environment and Urban Systems, 86, 101591. https://doi.org/10.1016/j.compenvurbsys.2020.101591
Autermann, C. (2019, September 9). WPS Routing API ER. OGC Meeting, WPS/Workflow session, Banff (Remote).
Autermann, C. (2022, August 30). GeoNode as Research Data Infrastructure [Lecture]. OpenGeoHub Summer School, Siegburg.
Autermann, C. (2024, May 7). KI:STE - Nutzung von künstlicher Intelligenz für fundierte Umweltdatenanalysen und einer gemeinsam definierten KI-Plattform. KI-Workshops des Bundesamtes für Kartographie und Geodäsie (BKG) und des Umweltbundesamtes (UBA), House of Logistics and Mobility (HOLM), Frankfurt am Main. https://evrs.bkg.bund.de/SharedDocs/Downloads/BKG/DE/Downloads-Veranstaltungen/KI-Workshop-Tagungsband.pdf;jsessionid=6CB65DCC72C865090E49AA00C9EC992C.live12?__blob=publicationFile&v=1
Autermann, C., Jirka, S., & Schaap, D. (2019, April 7). The SeaDataCloud Sensor Web Approach [Poster]. EGU General Assembly 2019, Vienna, Austria. https://meetingorganizer.copernicus.org/EGU2019/EGU2019-13984-1.pdf
Autermann, C., Jirka, S., & Schaap, D. (2021). Interoperable Provision of Research Vessel Tracking Data via OGC SensorThings API and Sensor Observation Service. IMDIS 2021 - International Conference on Marine Data and Information Systems - Book of Abstracts, Vol. 62, 300–301. https://imdis.seadatanet.org/content/download/151922/file/IMDIS2021_proceedings.pdf
Bredel, H., Jirka, S., Masó Pau, J., & Piera, J. (2020, May 4). Design and Development of Interoperable Cloud Sensor Services to Support Citizen Science Projects [Display]. EGU General Assembly 2020, Online. https://doi.org/10.5194/egusphere-egu2020-13338
Britsch, C., Kügeler, A., & Jirka, S. (2023, September 8). Geospatial trends 2023: Opportunities for data.europa.eu [Webinar]. https://www.youtube.com/watch?v=YYiMQOQpk8A
Britsch, C., Kügeler, A., & Jirka, S. (2023). Geospatial Trends 2023: Opportunities for data.europa.eu from emerging trends in the geospatial community. Publications Office of the European Union. https://data.europa.eu/sites/default/files/report/Geospatial_trends_report_2023_EN.pdf
Bröring, A., & Jirka, S. (2011, April 18). Sensor Web In Practice [Oral]. Workshop on GI Research and instruction mixing commercial and Open Source Tools (GIROST) - at the AGILE 2011 - 14th AGILE International Conference on Geographic Information Science, Utrecht, The Netherlands.
Bröring, A., Echterhoff, J., Jirka, S., Simonis, I., Everding, T., Stasch, C., Liang, S., & Lemmens, R. (2011). New Generation Sensor Web Enablement. Sensors, 11(3), 2652–2699. https://doi.org/10.3390/s110302652
Buck, J. J. H., Bainbridge, S. J., Burger, E. F., Kraberg, A. C., Casari, M., Casey, K. S., Darroch, L., Rio, J. D., Metfies, K., Delory, E., Fischer, P. F., Gardner, T., Heffernan, R., Jirka, S., Kokkinaki, A., Loebl, M., Buttigieg, P. L., Pearlman, J. S., & Schewe, I. (2019). Ocean Data Product Integration Through Innovation-The Next Level of Data Interoperability. Frontiers in Marine Science, 6, 32. https://doi.org/10.3389/fmars.2019.00032
Buck, J. J. H., Pearlman, J., Bainbridge, S. J., Burger, E. F., Buttigieg, P. L., Casari, M., Casey, K. S., Darroch, L., del Rio, J., Delory, E., Gardner, T., Jirka, S., & Kokkinaki, A. (2019, December 9). Data reusability and its role in the democratization of marine data [Oral]. AGU Fall Meeting 2019, San Francisco, CA, USA. https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/619313
Buurman, M., Konkol, M., Bremerich, V., & Domisch, S. (2024). The AquaInfra VRE: A platform to perform freshwater and marine research online. https://doi.org/https://doi.org/10.13127/MISC/80
de Wall, A., Remke, A., Gräler, B., Fechner, T., & Bernsdorf, B. (2019, April 7). Improving Methods and Technologies for the Large-Scale Monitoring of Flooding Situations [PICO]. EGU General Assembly 2019, Vienna, Austria. https://meetingorganizer.copernicus.org/EGU2019/EGU2019-16365-1.pdf
de Wall, A., Remke, A., & Gräler, B. (2019, April 7). An Cloud-native Architecture for Near-Real-Time Monitoring of Floods [Oral]. EGU General Assembly 2019, Vienna, Austria. https://meetingorganizer.copernicus.org/EGU2019/EGU2019-9476-1.pdf
de Wall, A., Danowski-Buhren, C., Wytzisk-Arens, A., Lingemann, K., Focke Martinez, S., Gandorfer, M., Meyer-Aurich, A., Bernhardt, H., Maidl, F. X., Fröhlich, G., & Floto, H. (2020). prospective.HARVEST – Optimizing Planning of Agricultural Harvest Logistic Chains. Referate Der 40. GIL-Jahrestagung, P-299, 55–60. https://dl.gi.de/handle/20.500.12116/31927
de Wall, A., Remke, A., Bernsdorf, B., Fechner, T., Bicsan, A., & Klink, A. (2021). SenSituMon – Integration von Satelliten- und In-situ-Sensordaten zur Verbesserung der automatisierten großräumigen Überwachung von Überflutungen. zfv – Zeitschrift für Geodäsie, Geoinformation und Landmanagement, 3/2021, 168–178. https://doi.org/10.12902/zfv-0343-2021
Demmich, K. (2023, April 28). Building an Open Source Infrastructure for Next Generation End User Climate Services [Orals]. EGU General Assembly 2023, Vienna. https://meetingorganizer.copernicus.org/EGU23/EGU23-15863.html
Devaraju, A., Jirka, S., Kunkel, R., & Sorg, J. (2015). Q-SOS - A Sensor Observation Service for Accessing Quality Descriptions of Environmental Data. ISPRS International Journal of Geo-Information, 4(3), 1346–1365. https://doi.org/10.3390/ijgi4031346
Drost, S. (2021, April 16). WaCoDiS & CODE-DE: The future (of Earth Observation data processing) is now. https://blog.52north.org/2021/04/16/wacodis-code-de-the-future-of-earth-observation-data-processing-is-now/
Drost, S., & Danowski-Buhren, C. (2023). KomMonitor - Kommunales Monitoring zur Raumentwicklung. FOSSGIS 2023 Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, Open Data und OpenStreetMap. https://doi.org/10.5446/61098
Drost, S., Rieke, M., Jirka, S., Vogt, A., Kirstein, V. R., & Wytzisk, A. (2019, June 17). An Event-Driven Architecture Based on Copernicus Satellite Data for Water Monitoring. Accepted Short Papers and Posters from the 22nd AGILE Conference on Geo-Information Science. AGILE Conference 2019, Limassol, Cyprus. https://agile-online.org/images/conference_2019/documents/short_papers/68_Upload_your_PDF_file.pdf
Drost, S., Wytzisk, A., & Remke, A. (2018, June 12). Geocoding of Crisis Related Social Media Messages for Assessing Voluntary Help Efforts as a Contribution to Situational Awareness. Geospatial Technologies for All : Short Papers, Posters and Poster Abstracts of the 21th AGILE Conference on Geographic Information Science. 21th AGILE Conference on Geographic Information Science, Lund, Sweden. https://agile-online.org/conference_paper/cds/agile_2018/shortpapers/69%20AGILE_2018_Geocoding_Social_Media_Messages.pdf
Drost, S., Danowski-Buhren, C., Vogt, A., & Rieke, M. (2020, September 31). Distributed Services for Enabling Automated Processing of Big EO Data [Oral]. Geospatial Sensing Virutal 2020, Münster, Germany. https://52north.org/delivery/GSV2020/presentations/Drost_Distributed_Services_for_Enabling_Automated_Processing_of_Big_EO_Data.pdf
Drost, S., Speckamp, J., Hollmann, C., Malewski, C., Rieke, J., & Jirka, S. (2020, May 4). Internet of Things Technologies for the Efficient Collection of Hydrological Measurement Data [Display]. EGU General Assembly 2020, Online. https://doi.org/10.5194/egusphere-egu2020-10452
Drost, S., Speckamp, J., Hollmann, C., Malewski, C., Rieke, M., & Jirka, S. (2020). Internet of Things Technologies for the Efficient Collection of Hydrological Measurement Data [Other]. pico. https://doi.org/10.5194/egusphere-egu2020-10452
Drost, S., Netzel, F., Wytzisk-Ahrens, A., & Mudersbach, C. (2021, April 19). The Impact of Land Cover Data on Rainfall-Runoff Prediction Using an Entity-Aware-LSTM [PICO]. EGU General Assembly 2021. https://doi.org/10.5194/egusphere-egu21-1136
Drost, S., Vogt, A., Danowski-Buhren, C., Jirka, S., Kirstein, V., Pakzad, K., & Rieke, M. (2022). WaCoDiS: Automated Earth Observation data processing within an event-driven architecture for water monitoring. Computers & Geosciences, 159. https://doi.org/10.1016/j.cageo.2021.105003
Drost, S., Kirstein, V., Pakzad, K., Jirka, S., & Wytzisk-Arens, A. (2022). WaCoDiS – Erleichterte Nutzung von Satellitendatenplattformen am Beispiel eines verbesserten Gewässermonitorings. Zfv – Zeitschrift Für Geodäsie, Geoinformation Und Landmanagement, 2/2022, 104–115. https://doi.org/10.12902/zfv-0378-2021
Drost, S., Malewski, C., & Jirka, S. (2022). Sensor Web and Internet of Things Technologies for Hydrological Measurement Data. In A. Di Mauro, A. Scozzari, & F. Soldovieri (Eds.), Instrumentation and Measurement Technologies for Water Cycle Management (pp. 279–293). Springer International Publishing. https://doi.org/10.1007/978-3-031-08262-7_12
Drost, S., Rohling, I., & Danowski-Buhren, C. (2024). Virtuelle Erreichbarkeitsanalysen in KomMonitor zur kommunalen und sozialräumlichen Bedarfsplanung. FOSSGIS 2024 Anwenderkonferenz für Freie und Open Source Software für Geoinformationssysteme, Open Data und OpenStreetMap.
Fechner, T., Remke, A., & Rath, C. (2019, November 27). Copernicus Daten Infrastruktur @ IT.NRW – Modulare und skalierende Copernicus Datenverarbeitung. 7. Deutsches Geoforum 2019, Berlin.
Fuchs, S., Hilgert, S., Sotiri, K., Wagner, A., Ishikawa, M., Kern, J., Jirka, S., Klassen, I., Krumm, J., Malewski, C., Rohr, H., & Pakzad, K. (2019). Sustainable management of reservoirs - defining minimum data needs and model complexity. Conference Proceedings, 12–15. https://bmbf-grow.de/sites/bmbf-grow.de/files/documents/webkomprimiert_proceedings_grow_midterm_conference_web_final_webkomprimiert.pdf
Gräler, B. (2019, July 10). How to model skewed and heavy tailed distributions: An introduction to copulas [Workshop]. Spatial Statistics Conference, Sitges, Spain. https://www.elsevier.com/events/conferences/spatial-statistics
Gräler, B. (2022, August 31). Extremes [Lecture]. OpenGeoHub Summer School, Siegburg.
Gräler, B., & Bredel, H. (2023). GeoNode in Forschungsdateninfrastrukturen. https://doi.org/10.5446/61149
Gräler, B., de Wall, A., & Remke, A. (2019, April 7). Citizen Science for Traffic Management and Safety [Poster]. EGU General Assembly 2019, Vienna, Austria. https://meetingorganizer.copernicus.org/EGU2019/EGU2019-15704.pdf
Gräler, B., Malewski, C., Förster, C., & Jirka, S. (2019, February 20). Integration zeitlich hochauflösender Fernerkundungsdaten in das Sensor Web [Oral]. 2. Bochumer Hydrometrie-Kolloquium, Bochum, Germany.
Gräler, B., Stumpen, H. A., Klatt, I., Pontius, M., & Remke, A. (2020, March 12). PreASiSt – Predictive Analytics zur Verbesserung der Sicherheit im Straßenverkehr. mFund-Fachaustaustausch: Verkehrssicherheit, DHPol, Münster.
Gräler, B., Klatt, I. I., Pontius, M., & Remke, A. (2020). Predictive Analytics to Improve Road Safety. 2020 10th International Conference on Advanced Computer Information Technologies (ACIT), 362–367. https://doi.org/10.1109/ACIT49673.2020.9208848
Gräler, B., Stasch, C., Proß, B., Peeters, O., & Jirka, S. (2017, November 6). Open Tools for Statistical Analysis in Spatial Data Infrastructures. UNECE Workshop on Integrating Geospatial and Statistical Standards 2017, Stockholm, Sweden. http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.58/2017/mtg3/article_52N_final.pdf
Gräler, B., Doll, C., Mück, J., Remke, A., Schramm, D., de Wall, A., & Wulffius, H. (2020, March 23). CITRAM - Citizen Science for Traffic Management [Other]. https://doi.org/10.5194/egusphere-egu2020-19952
Gräler, B., Autermann, C., & Rieke, M. (2022, January 6). Die Rolle des Geoinformationsmanagements im Projekt KI:STE: KI Strategie für Erdsystemdaten. GeoIT Round Table NRW, Düsseldorf. https://www.gdi.nrw/organisation/geoit-round-table-nrw/sitzungen
Granell, C., Mooney, P., Jirka, S., Rieke, M., Ostermann, F., van den Broecke, J., Sarretta, A., Verhulst, S., Dencik, L., Oost, H., Micheli, M., Minghini, M., Kotsev, A., & Schade, S. (2022). Emerging approaches for data-driven innovation in Europe (JRC Technical Report No. JRC127730; p. 130). Jount Research Centre. https://data.europa.eu/doi/10.2760/630723
Grossmann, Y. V., & Franke, M. (2023). Software ist kein Beiprodukt!
Jirka, S. (2013). Discovery Mechanisms for the Sensor Web (Vol. 11). IOS Press/AKA Verlag.
Jirka, S. (2010, March 9). Sensor Web Enablement Tutorial [Tutorial]. 15. Münchner Fortbildungsseminar Geoinformationssysteme, München, Germany.
Jirka, S. (2010, April 27). SOS Adapter für ArcGIS - Sensor Web Enablement [Oral]. ESRI 2010 - 16. Deutschsprachige ESRI Anwenderkonferenz, Darmstadt, Germany.
Jirka, S. (2009, March 11). Mainstreaming Sensor Web Enablement Technologies Through Collaboration [Oral]. III Jornadas de SIG Libre, Girona, Spain.