## 1 Introduction
If a chromatic CV \(\supset\) EO-IU system does not down-scale seamlessly to achromatic image analysis, then it tends to ignore the paramount spatial information in favor of subordinate (secondary) spatial context-insensitive color information, such as MS signatures typically investigated in traditional pixel-based single-date or multi-temporal EO-IU algorithms. In other words, a necessary and sufficient condition for a CV \(\supset\) EO-IU system to fully exploit primary spatial topological information (e.g., adjacency, inclusion, etc.) and spatial non-topological information (e.g., spatial distance, angle distance) components, in addition to secondary colorimetric information, is to perform nearly as well when input with either panchromatic or color imagery [13]. Underpinned by this background knowledge about the cognitive process of _vision_, an undisputable true fact is that ESA EO Level 2 product generation is an inherently ill-posed CV problem (_chicken-and-egg dilemma_), whose inherently ill-posed CV sub-problem is cloud and cloud-shadow quality layers detection. Since it is inherently ill-posed, ESA EO Level 2 information product generation is, first, very difficult to solve; in fact, no ESA EO Level 2 product has ever been accomplished in an operating mode by any EO data provider at the ground segment to date. Second, it requires _a priori_ knowledge in addition to sensory data to become better conditioned for numerical solution [32; 33]. Our conclusion is that systematic ESA EO Level 2 product generation, at the core of the present work, is of potential interest to relevant portions of the RS community, involved with EO _big data_ transformation into timely, comprehensive and operational EO data-derived VAPS [3; 5]. Regarded as necessary-but-not-sufficient pre-condition for a GEOSS to cope with the five Vs of EO _big data_ analytics, see Figure 4, systematic ESA EO Level 2 product generation is still open for solution in operating mode. Figure 7: Semantics-enabled EO _big data cube_, synonym for artificial intelligence (AI) for Data and Information Access Services (AI4 DIAS). Each single-date EO Level 1 source image, radiometrically calibrated into top-of-atmosphere reflectance (TOARF) values and stored in the database, is automatically transformed into an ESA EO Level 2 product comprising: (i) a single-date multi-spectral (MS) image radiometrically calibrated from TOARF into surface reflectance (SURF) values, corrected for atmospheric, adjacency and topographic effects, stacked with (ii) its EO data-derived value-adding scene classification map (SCM), equivalent to a sensory data-derived categorical/nominal/qualitative variable of semantic quality, where the thematic map legend is general-purpose, user- and application-independent and comprises quality layers, such as cloud and cloud–shadow. It is eventually stacked with (iii) its EO data-derived value-adding numeric variables, such as biophysical variables, e.g., leaf area index (LAI) [2; 131], class-conditional spectral indexes, e.g., vegetation class-conditional greenness index [132; 133], categorical variables of sub-symbolic quality (geographic field-objects), e.g., fuzzy sets/discretization levels low/medium/high of a numeric variable, etc. [134]. As the second original contribution of this review section, the several degrees of novelty of the ESA EO Level 2 product definition (refer to Section 1) are described below. First, the ESA EO Level 2 product definition is innovative because it overtakes the traditional concept of EO data cube with an innovative EO data cube stacked with its data-derived value-adding information cube, synonym for semantics-enabled EO data cube or AI4 DIAS, see Figure 7. A semantics-enabled EO data cube is alternative to existing EO data cubes, affected by the so-called data-rich information-poor (DRIP) syndrome [135], such as the existing first generation of the European Commission (EC) Data and Information Access Services (DIAS) [136; 137].