2019
DOI: 10.3390/data4030102
|View full text |Cite
|
Sign up to set email alerts
|

Semantic Earth Observation Data Cubes

Abstract: There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a sp… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
24
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
9
1

Relationship

1
9

Authors

Journals

citations
Cited by 56 publications
(24 citation statements)
references
References 47 publications
0
24
0
Order By: Relevance
“…We may argue that whenever greater tasks need to be taken over by AI, then knowledge-based solutions based spatio-temporal properties may be a small, yet critical element in them. For example, the provision of a (low-level) semantic data cube, where for each observation (pixel) at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance [89] has an enormous potential to be further enriched by spatial concepts. If implemented and upscaled, we may then move from image-specific solutions and case-by-case optimisations of algorithms towards more adaptive learning systems, in other words starting from a "seed AI" [90] and move towards more holistic image-based decision systems.…”
Section: Discussionmentioning
confidence: 99%
“…We may argue that whenever greater tasks need to be taken over by AI, then knowledge-based solutions based spatio-temporal properties may be a small, yet critical element in them. For example, the provision of a (low-level) semantic data cube, where for each observation (pixel) at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance [89] has an enormous potential to be further enriched by spatial concepts. If implemented and upscaled, we may then move from image-specific solutions and case-by-case optimisations of algorithms towards more adaptive learning systems, in other words starting from a "seed AI" [90] and move towards more holistic image-based decision systems.…”
Section: Discussionmentioning
confidence: 99%
“…In [9], QB has been used for publishing tabular time series data and for structuring it into slices to support multiple views on the data. In a way similar to a spatio-temporal data cube, the semantic EO data cube [10] contains EO data where for each observation at least one nominal (i.e., categorical) interpretation is available. Closer to our work but with an ontology-based data access (OBDA) approach, the model used in [11] extends three ontologies, namely Data Cube, GeoSPARQL, and OWL-Time, to offer access to Copernicus services.…”
Section: Semantic Models and Semantic Etl Processes For Eo Data Integmentioning
confidence: 99%
“…Alternatively, low-level fusion is insufficiently addressed. The data cube paradigm (Augustin et al, 2019) in the multi-modal case is limited to the stack-and-classify approach, which consists in resampling all data to the highest resolution, either at ingestion time or at query time. In case of very distinct modalities, super-resolution or disaggregation techniques based on the underlying physics are preferred, pan-sharpening solutions being the most widespread.…”
Section: Optimal Exploitation Of Data Sourcesmentioning
confidence: 99%