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.

ohsome Platform

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.


OpenStreetMap History Database (OSHDB) – efficient storage of and access to OpenStreetMap’s data history
OSM History Explorer (ohsomeHeX) – spatio-temporal exploration of OpenStreetMap data on a global scale
ohsome API – extraction and analysis of OpenStreetMap data history via HTTP requests.
ohsome quality API (OQAPI) – OpenStreetMap data quality for specific regions and use cases
ohsome Dashboard – statistics on the historical development of OpenStreetMap data
Humanitarian OSM Stats – statistics and graphs concerning mapping in OpenStreetMap for humanitarian purposes

Cooperation Projects with HeiGIT Support


IDEAL VGI – Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information
OSM Landuse Landcover – explore the OpenStreetMap database specifically in terms of landuse and landcover information
Climate Action California – shaping climate action in a sound way – case Study Baden-Württemberg/California

Completed Projects
Global Exposure Data for Risk Assessment – global disaster risk reduction dataset
Healthsites Quality – assessing the quality of health-related data in OpenStreetMap
ohsome2X – create the data for time-series maps of OpenStreetMap’s historic development
ohsome2label – historical OpenStreetMap objects as machine learning training samples
ohsome-py – a Python client for the ohsome API

Completed Cooperation Projects with HeiGIT Support


WIN project “Shared Data Sources” – importance of cognitive coherence in collective decision making
Waterproofing Data
– investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices
DFG-OSM-Quality – repository for OSM data quality measures
LandSense – a citizen observatory and innovation marketplace for land use and land cover monitoring (EU Horizon 2020).
Global Climate Protection Map – spatial location and retrieval of information on the topics of energy transition and sustainability
Intrinsic OSM-Quality – framework for measuring the fitness for purpose of OpenStreetMap data based on intrinsic quality indicators

Additional Services


This link will take you to an overview listing all our academic articles: Publications.