The Sen2Cube.at national semantic Earth observation data cube for Austria

The Sen2Cube.at is a Sentinel-2 semantic Earth observation (EO) data and information cube that combines an EO data cube with an AI-based inference engine by integrating a computer-vision approach to infer new information. Our approach uses semantic enrichment of optical images and makes the data and information directly available and accessible for further use within an EO data cube. The architecture is based on an expert system, in which domain-knowledge can be encoded in semantic models (knowledgebase) and applied to the Sentinel-2 data as well as semantically enriched, data-derived information (factbase).  

The initial semantic enrichment in the Sen2Cube.at system is general-purpose, user- and application-independent, derived directly from optical EO images as an initial step towards a scene classification map. These information layers are automatically generated from Sentinel-2 images with the SIAM software (Satellite Image Automated Mapper). SIAM is a knowledge-based and physical-model-based decision tree that produces a set of information layers in a fully automated process that is applicable worldwide and does not require any samples. A graphical inference engine allows application-specific Web-based semantic querying based on the generic information layer as a replicable and explainable approach to produce information. The graphical inference engine is a new Browser-based graphical user interface (GUI) developed in-house with a semantic querying language. Users formulate semantic models in a graphical way and can execute them on any area-of-interest and time interval, which will be evaluated by the core of the inference engine attached to the data cube. This also enables non-expert users to formulate analyses without requiring programming skills.  

While the methodology is software-independent, the prototype is based on the Open Data Cube and additional in-house developed components in the Python programming language. Scaling is possible depending on the available infrastructure resources due to the system’s Docker-based container architecture. Through its fully automated semantic enrichment, innovative graphical querying language in the GUI for semantic querying and analysis as well as the implementation as a scalable infrastructure, this approach is suited for big data analysis of Earth observation data. It was successfully scaled to a national data cube for Austria, containing all available Sentinel-2 images from the platforms A and B.