Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as <i>necessary-but-not-sufficient</i> precondition of the downstream in a new notion of Space Economy 4.0 - Part 1: Problem background in Artificial General Intelligence (AGI)
Creators: name: Baraldi, Andrea
name: Sapia, Luca D.
name: Tiede, Dirk
name: Sudmanns, Martin
name: Augustin, Hannah L.
name: Lang, Stefan
Publisher: name: Taylor & Francis
Year: 2022
Subjects: subject: Biochemistry
subject: Space Science
subject: Neuroscience
subject: Chemical Sciences not elsewhere classified
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Science Policy

description: Aiming at the convergence between Earth observation (EO) <i>Big Data</i> and Artificial General Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical sensory image-derived semantics-enriched Analysis Ready Data (ARD) product-pair and process gold standard as linchpin for success of a new notion of Space Economy 4.0. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO <i>big data</i> providers, it is regarded as <i>necessary-but-not-sufficient “horizontal”</i> (enabling) precondition for: (I) Transforming existing EO <i>big</i> raster-based <i>data cubes</i> at the midstream segment, typically affected by the so-called data-rich information-poor syndrome, into a new generation of semantics-enabled EO <i>big raster-based numerical data</i> and <i>vector-based categorical</i> (symbolic, semi-symbolic or subsymbolic) <i>information cube</i> management systems, eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery. (II) Boosting the downstream segment in the development of an ever-increasing ensemble of <i>“vertical”</i> (deep and narrow, user-specific and domain-dependent) value–adding information products and services, suitable for a potentially huge worldwide market of institutional and private end-users of space technology. For the sake of readability, this paper consists of two parts. In the present Part 1, first, background notions in the remote sensing metascience domain are critically revised for harmonization across the multi-disciplinary domain of cognitive science. In short, keyword <i>“information”</i> is disambiguated into the two complementary notions of quantitative/unequivocal <i>information-as-thing</i> and qualitative/equivocal/inherently ill-posed <i>information-as-data-interpretation</i>. Moreover, buzzword <i>“artificial intelligence”</i> is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as <i>part-without-inheritance-of</i> AGI. Second, based on a better-defined and better-understood vocabulary of multidisciplinary terms, existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system. To overcome their drawbacks, an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.