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 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. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.
Highlights
The current Earth observation (EO) data pool is vastly different than a mere decade ago, but the main challenge remains: to produce information from data to generate knowledge [1,2]
We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data
Semantic EO data cubes are interdisciplinary in their conceptualisation, combining concepts related to image retrieval, computer vision, human cognition, semantics, world ontologies, remote related to image retrieval, computer vision, human cognition, semantics, world ontologies, remote sensing and more
Summary
The current Earth observation (EO) data pool is vastly different than a mere decade ago, but the main challenge remains: to produce information from data to generate knowledge [1,2]. We are surrounded by a growing ocean of EO data, but sensory data are not information and have no inherent meaning (i.e., lacking semantics) without some form of interpretation. At a minimum, this data pool is characterised by a rapidly growing data volume, accelerating data velocity (i.e., increasing data acquisition and processing speeds) and an increasingly diverse variety of sensors and products [3]. The definitions or specifications of EO data cubes will not be discussed here but can be understood as a way of organising EO data using a logical view on them, either based on an existing archive (i.e., “indexing”) or a specific, application-optimised, multi-dimensional data structure (i.e., “ingestion”). The logical view refers to the way of accessing EO data by using spatio-temporal coordinates either in an application programming interface (API) or a query language instead of file
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