I am a geospatial data scientist and engineer who likes to derive actionable insights from big spatial data. In my approach, I combine a Discrete Global Grid System to organize, merge,
analyze and visualize large spatial datasets at multiple scales and Artificial Neural Networks (ANN) to crunch through data to make predictions. A data platform such
as a DGGS and machine learning algorithms like ANNs help to produce insights from big earth observation data, location-based services (LBS), internet of things (IoT), volunteered geographic information (VGI) from
citizens, and other sensors.
I studied Geography and Geomatics at Ruhr-University Bochum in Germany and joined the UN Operational Satellite Applications Program (UNOSAT) of the United Nations Institute for Training and Research (UNITAR) in Geneva, Switzerland in 2009. I worked there for about 1.5 years before moving to the State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing (LIESMARS) at Wuhan University, China. There I pursued my doctoral degree in engineering in photogrammetry and remote sensing and worked as a Postdoc until November 2019. In 2013/2014 I did a contract engagement at the Global Polio Eradication Initiative at the World Health Organization HQ in Geneva, which set the frame for my future research interests and endeavors.
The question that comes up when dealing with complex geographic problems, such as predicting
epidemics, crime, or ghost towns is: How to integrate geographic data to take advantage of multiple sources to address complex geographic problems?
My vision is to create an Intelligent Earth that knows what is where, what was there before, and what might be there. In a holistic approach, I combine multiple data sources in a Discrete Global Grid System (DGGS).
This structures and harmonizes data from various sources such as satellite imagery, social media, census, or any data that has a spatial reference into hexagonal cells of almost equal size. As each phenomenon has various spatial
effects at different resolutions, it is necessary to include multi-scale capabilities.
Each cell in the grid has a unique ID. Stored in a relational database system RDBMS such as PostgreSQL + PostGIS, Accumolo + GeoMESA this framework becomes a powerful tool for big geospatial data management and analysis.