## 1 Introduction
Deductive inference is static (non-adaptive to data) and typically lacks flexibility to transform ever-varying sensory data (sensations) into stable percepts (concepts) in a world model [13; 23; 82; 83]. In compliance with the GEO-CEOS QA4 EO _Cal_/_Val_ requirements and the visionary goal of a GEOSS [5], ESA has recently provided an original ESA EO Level 2 information product definition, refer to Section 1[11; 12]. The ESA EO Level 2 product definition is non-trivial. Notably, it is more restrictive than the National Aeronautics and Space Administration (NASA) EO Level 2 product definition of \"a data-derived geophysical variable at the same resolution and location as Level 1 source data\" [109]. According to the standard Unified Modeling Language (UML) for graphical modeling of object-oriented software [86], where symbol '\(\rightarrow\)' denotes relationship _part-of_ pointing from the supplier to the client (vice versa, it would denote relationship _depend-on_), not to be confused with relationship _subset-of_, whose symbol is '\(\supset\)', meaning specialization with inheritance from the superset to the subset, the following dependence relationship holds true:  
'NASA EO Level 2 product \(\rightarrow\) ESA EO Level 2 product'. Depicted in Figure 4, this dependence relationship implies that a NASA EO Level 2 product can be accomplished although no ESA EO Level 2 product exists, whereas the vice versa does not hold. Figure 4: In agreement with the standard Unified Modeling Language (UML) for graphical modeling of object-oriented software [86], relationship _part-of_, denoted with symbol ‘\(\rightarrow\)’ pointing from the supplier to the client, should not to be confused with relationship _subset-of_, ‘\(\supset\)’, meaning specialization with inheritance from the superset to the subset. A National Aeronautics and Space Administration (NASA) EO Level 2 product is defined as “a data-derived geophysical variable at the same resolution and location as Level 1 source data” [109]. Herein, it is considered _part-of_ an ESA EO Level 2 product defined as [11; 12]: (a) a single-date multi-spectral (MS) image whose digital numbers (DNs) are radiometrically corrected into surface reflectance (SURF) values for atmospheric, adjacency and topographic effects, stacked with (b) its data-derived general-purpose, user- and application-independent scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. In this paper, ESA EO Level 2 product is regarded as an information primitive to be accomplished by Artificial Intelligence for the Space segment (AI4 Space), such as in future intelligent small satellite constellations, rather than at the ground segment in an AI for Data and Information Access Services (AI4 DIAS) framework. In this graphical representation, additional acronyms of interest are computer vision (CV), whose special case is EO image understanding (EO-IU) in operating mode, semantic content-based image retrieval (SCBIR) [13; 110; 111; 112; 113; 114; 115], semantics-enabled information/knowledge discovery (SEIKD), where SCIR + SEIKD is considered synonym for AI4 DIAS, and Global Earth Observation System of Systems (GEOSS), defined by the Group on Earth Observations [5]. Our working hypothesis postulates that the following dependence relationship holds true. ‘NASA EO Level 2 product \(\rightarrow\) ESA EO Level 2 product = AI4 Space \(\subset\) EO-IU in operating mode \(\subset\) CV \(\rightarrow\) [EO-SCBIR + SEIKD = AI4 DIAS] \(\rightarrow\) GEO-GEOSS’. This equation means that GEOSS, whose _part-of_ are the still-unsolved (open) problems of SCBIR and SEIKD, cannot be achieved until the necessary-but-not-sufficient pre-condition of CV in operating mode, specifically,  
systematic ESA EO Level 2 product generation, is accomplished in advance. Encompassing both biological vision and CV, _vision_ is synonym for scene-from-image reconstruction and understanding.