Background
Recent
developments in satellite technology allow image acquisition with a high
temporal frequency, yielding several tens of images per year for many
geographical locations. Current approaches need to be adapted, e.g. for
handling long Earth observation (EO) image time series. Data cubes are one solution to tackle this, one specific development are "semantic EO data cubes" with a first prototype of an operational Sentinel-2 semantic Earth observation (EO) data cube for Austria developed at Z_GIS.
Short description
A semantic EO data cube was defined as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance (see Augustin et al, 2019).
This approach allows human users to query and analyse EO data on a higher semantic level (i.e. based on at least basic land cover units and encoded ontologies) directly in the data cube. A unique Web-interface allows interactive human-like queries based on spatio-temporal and semantic relationships. The new architecture allows a plethora of different approaches / query-models to be developed and applied to different use cases. Master theses could cover different topics making use of the generic semantic data cube for Austria (covering all Sentinel-2 data acquired) and may focus on the development of spatio-temporal queries for different use cases or on further development of parts of the system.
This approach allows human users to query and analyse EO data on a higher semantic level (i.e. based on at least basic land cover units and encoded ontologies) directly in the data cube. A unique Web-interface allows interactive human-like queries based on spatio-temporal and semantic relationships. The new architecture allows a plethora of different approaches / query-models to be developed and applied to different use cases. Master theses could cover different topics making use of the generic semantic data cube for Austria (covering all Sentinel-2 data acquired) and may focus on the development of spatio-temporal queries for different use cases or on further development of parts of the system.
Suggested reading
Tiede, D., Baraldi, A., Sudmanns, M., Belgiu, M., Lang, S., 2017. Architecture and Prototypical Implementation of a Semantic Querying System for Big Earth Observation Image Bases. Eur. J. Remote Sens. 50, 452–463.Augustin, H., Sudmanns, M., Tiede, D., Lang, S., Baraldi, A., 2019. Semantic Earth Observation Data Cubes. Data 4. https://doi.org/10.3390/data4030102
Start
Anytime
Prerequisites/qualification
Interest in the topic++
Databases+
Programming or scripting languages++
Remote sensing++
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