Big Spatial Data Analytics
What we do
We live in a time where vast amounts of spatial data are generated by technical sensors, social media users, and volunteers via crowdsourcing. Processing such large data poses complex challenges due to their mere volume and semantic complexity. We help by rendering these datasets usable for your application, while always taking into account the spatial context. Our long experience gathered in numerous research projects enables us to serve as an interface between technology and its application. Based on your specific needs, we develop processes and tools for assessing the quality and enriching heterogeneous Web 2.0 data by applying innovative methods from spatial data mining and deep learning.
OpenStreetMap data are continuously amended, updated, and corrected. All changes are saved to ensure full traceability. The ohsome platform enables easy access to the full history of OpenStreetMap – worldwide and precise to the second. This way, all (historical) OpenStreetMap elements ever recorded can be reconstructed and analyzed. One important objective is the improved assessment of the quality and usability of OpenStreetMap for your individual application.
Big Data Technology
We deploy big data technology and cluster computing to enable parallel data processing on a scalable server cluster.
Integration through API
Our programming interfaces can be integrated into different systems. This feature enables users to implement their own customized analyses based on the OpenStreetMap history.
Intrinsic Quality Assessment
Based on the ohsome platform, we provide intrinsic quality indicators to effectively support the data quality assessment for various applications.
Deep VGI (Deep Learning with Volunteered Geographic Information) connects user-generated geodata with machine-based learning. Learning algorithms are already successfully used in the field of geoinformation; however, the required training data are often scarce, particularly for rural areas and developing countries. This gap is filled by volunteered geographic information, which helps us optimize the machine-based identification of buildings on satellite images.
Wealth of Information
We use OpenStreetMap data and MapSwipe data due to their abundance and comprehensiveness, making them an ideal foundation for improving the precision of machine-based learning algorithms. We extract relevant information and gain a better understanding of spatial structures and processes.
Crowd and Machine
Analysis tasks involving the automated gathering of information support users and free human analysis skills for tasks that can’t be automated. The consequent savings of time and resources is particularly relevant in time-sensitive situations, such as the mapping efforts for destroyed infrastructure in the aftermath of disaster events.
Improve Data Quality
Machine-based learning is another option for analyzing geodata quality. The automatically generated results help us better understand the user-generated geodata, and to quickly and reliably identify areas with “good” and “poor” geodata.
|– efficient storage of and access to OpenStreetMap’s data history|
|– spatio-temporal exploration of OpenStreetMap data on a global scale|
|– extraction and analysis of OpenStreetMap data history via HTTP requests.|
|– OpenStreetMap data quality for specific regions and use cases|
|– statistics on the historical development of OpenStreetMap data|
|– create the data for time-series maps of OpenStreetMap’s historic development|
|– historical OpenStreetMap objects as machine learning training samples|
|– a Python client for the ohsome API|
|– global disaster risk reduction dataset|
|– statistics and graphs concerning mapping in OpenStreetMap for humanitarian purposes|
|– assessing the quality of health-related data in OpenStreetMap|
Cooperation Projects with HeiGIT Support
|– investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices|
|– importance of cognitive coherence in collective decision making|
|– Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information|
|– explore the OpenStreetMap database specifically in terms of landuse and landcover information|
|– shaping climate action in a sound way – case Study Baden-Württemberg/California|
Completed Cooperation Projects with HeiGIT Support
|– repository for OSM data quality measures|
|– a citizen observatory and innovation marketplace for land use and land cover monitoring (EU Horizon 2020).|
|– spatial location and retrieval of information on the topics of energy transition and sustainability|
|– framework for measuring the fitness for purpose of OpenStreetMap data based on intrinsic quality indicators|
Visualizing metadata (information about the data) not usually displayed in OpenStreetMap maps offers important intelligence in regards to the data quality. The web application ohsomeHeX provides such information rendered in a hexagonal raster, for instance detailing timeliness and completeness of OpenStreetMap data. The visual analysis of the resulting maps provides a first assessment of the data quality.
To get an impression of the impact that the work of thousands of volunteers had on OSM data in the Nepal region in 2015, a spatio-temporal distribution of contributor activity has been computed. This visualization is based on the ohsome platform.
OSM Landuse Landcover is a WebGIS application to explore the OpenStreetMap database specifically in terms of landuse and landcover information. This is based on our earlier work on testing the suitability of OpenStreetMap for deriving landuse and landcover information.
Generate accurate statistics about the historical development of OSM data for an arbitrary region.
HistOSM is a WebGIS application to visually explore historic objects stored in the OpenStreetMap database. Your visual exploration process is supported by dynamically created statistics showing the top most used object categories in the current map view extent.
The climate protection map Germany makes it easy to retrieve relevant information on the topics of energy transition or sustainability and to locate them spatially. As a part of the Urban Office (subproject 4) of the University of Heidelberg, the climate protection map Germany uses the possibilities of web technologies to find out about issues such as sustainable energy supply, mobility forms and consumption. Through spatial location, individual regions are made more comparable and a greater awareness of citizens' climate protection and energy transition will be created.