2022-08-26, 12:30–13:00 (Europe/Rome), General online
Geo Engine is a cloud-ready geospatial analysis platform that provides intuitive and low-threshold access to geospatial data, its processing, interfaces, and visualization. Users can access the engine in a browser-based user interface as well as with Jupyter notebooks in Python. An important element is the homogenized "Datacube"-like view of heterogeneous data, which allows research groups and companies easy access and low-threshold analyses. At the same time, it is a framework for the creation and operation of geodata portals.
The development is based on research results from the field of spatio-temporal data processing from the database systems group at the University of Marburg, Germany. It is currently used in scientific projects focused on environmental and biodiversity monitoring, where it provides native time series processing, the combination of raster and vector data, and a user interface that enables linked views between maps, tables, and plots. In addition, it is used for the provision of customized apps, for example for web-based remote-sensing learning and project portals.
The presentation gives an overview of the system and its features. The processing backend will be discussed, which allows tile-based (for raster data) or chunk-based (for vector data) processing, taking into account the time semantics of the data. In addition, we show a use case demonstration where we exemplify the seamless transition from Geo Engine’s UI to Python notebooks and also the step back. Finally, we give an overview of future development goals.
Christian Beilschmidt is a computer scientist and did his doctoral research at the University of Marburg, Germany, in the field of geodata processing and machine learning methods for the aggregation of spatio-temporal data. He was also substantially involved in the development of a web-based platform for spatio-temporal processing, explorative analysis and visualization of Big Spatial Data within projects on biodiversity and environmental monitoring. He is currently working in the EXIST research transfer project Geo Engine on a start-up that will further develop this platform into a cloud-ready service that bundles the integration and efficient processing of spatio-temporal data and intuitively opens up the latest visualization and analysis methods, such as deep learning.