The Sen2Cube.at national semantic Earth observation data cube for Austria&#160;
Authors: Sudmanns Martin , Augustin Hannah , van der Meer Lucas , Baraldi Andrea , Tiede Dirk 
Published: March 2021

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&lt;p&gt;The Sen2Cube.at is a Sentinel-2&amp;#160;semantic&amp;#160;Earth observation (EO)&amp;#160;data and information cube that combines an EO data cube with an AI-based inference engine by integrating&amp;#160;a&amp;#160;computer-vision&amp;#160;approach to&amp;#160;infer new&amp;#160;information.&amp;#160;Our approach uses semantic enrichment of optical images and makes the data and information directly available&amp;#160;and accessible&amp;#160;for further use within an EO data cube.&amp;#160;The architecture is based on an expert system, in which domain-knowledge&amp;#160;can be&amp;#160;encoded&amp;#160;in&amp;#160;semantic models (knowledgebase) and applied to the Sentinel-2 data as well as&amp;#160;semantically enriched,&amp;#160;data-derived information (factbase).&amp;#160;&amp;#160;&lt;/p&gt;
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&lt;p&gt;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).&amp;#160;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&amp;#160;applicable&amp;#160;worldwide&amp;#160;and does not require any samples.&amp;#160;A&amp;#160;graphical inference engine allows application-specific Web-based semantic querying&amp;#160;based on the generic information layer&amp;#160;as&amp;#160;a&amp;#160;replicable and explainable&amp;#160;approach to produce information.&amp;#160;The graphical inference engine is a&amp;#160;new&amp;#160;Browser-based graphical user interface&amp;#160;(GUI)&amp;#160;developed in-house&amp;#160;with a semantic querying&amp;#160;language.&amp;#160;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.&amp;#160;This&amp;#160;also&amp;#160;enables&amp;#160;non-expert users&amp;#160;to&amp;#160;formulate analyses&amp;#160;without&amp;#160;requiring&amp;#160;programming skills.&amp;#160;&amp;#160;&lt;/p&gt;
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&lt;p&gt;While the methodology is software-independent, the prototype is based on the Open Data Cube and&amp;#160;additional&amp;#160;in-house developed components&amp;#160;in&amp;#160;the Python&amp;#160;programming language.&amp;#160;Scaling is possible depending on the available infrastructure&amp;#160;resources&amp;#160;due&amp;#160;to the system&amp;#8217;s Docker-based container architecture.&amp;#160;Through its fully automated semantic enrichment, innovative graphical querying language&amp;#160;in the GUI&amp;#160;for semantic querying and analysis as well as the implementation as&amp;#160;a&amp;#160;scalable infrastructure,&amp;#160;this approach is suited for big data analysis of Earth observation data. It&amp;#160;was successfully scaled to a national data cube for Austria, containing all available Sentinel-2 images from&amp;#160;the&amp;#160;platforms&amp;#160;A&amp;#160;and&amp;#160;B.&amp;#160;&lt;/p&gt;
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