## 1 Introduction
In more detail, Equation (1) postulates that systematic ESA EO Level 2 product generation is an inherently ill-posed CV problem, where CV \(\supset\) EO-IU, whose solution in operating mode is necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) [13; 110; 111; 112; 113; 114; 115] and semantics-enabled information/knowledge discovery (SEIKD) in large-scale EO image data cubes, with SCBIR + SEIKD considered synonym for AI for Data and Information Access Services (AI4 DIAS), where AI4 DIAS is _part-of_ a yet-unaccomplished GEOSS. The closed-loop AI4 DIAS system architecture, suitable for semantics-enabled incremental learning [13; 115], is sketched in Figure 5 [13]. The working hypothesis (1) regards the ESA EO Level 2 information product as baseline information unit (information primitive) whose systematic generation is of paramount importance to contribute toward filling an analytic and pragmatic information gap from multi-sensor, multi-temporal and multi-angular EO _big image data cubes_ into timely, comprehensive and operational EO data-derived VAPS, in compliance with the visionary goal of a GEOSS [3, 5], unaccomplished to date. To justify our working hypothesis (1), let us introduce, first, the definition proposed for an EO-IU system to be considered in operating mode and, second, the background knowledge of _vision_ stemming from the multidisciplinary domain of cognitive science [77, 78, 79, 80, 18, 19, 78, 18]. Figure 5: Artificial intelligence (AI) for Data and Information Access Services (AI4 DIAS), synonym for semantics-enabled DIAS or closed-loop EO image understanding (EO-IU) for semantic querying (EO-IU4 SQ) system architecture. At the Marr level of system understanding known as system design (architecture) [76], AI4 DIAS is sketched as a closed-loop EO-IU4 SQ system architecture, suitable for incremental semantic learning. It comprises a primary (dominant, necessary-but-not-sufficient) hybrid (combined deductive and inductive) feedback (provided with feedback loops) EO-IU subsystem in closed-loop with a secondary (dominated) hybrid feedback EO-SQ subsystem. _Subset-of_ a computer vision (CV) system, where CV \(\supset\) EO-IU, the EO-IU subsystem is required to be automatic (no human–machine interaction is required by the CV system to run) and near real-time to provide the EO-SQ subsystem with useful information products, including thematic maps of symbolic quality, such as single-date ESA EO Level 2 Scene Classification Map (SCM) considered a necessary-but-not-sufficient pre-condition to semantic querying, synonym for semantics-enabled information/knowledge discovery (SEIKD) in massive multi-source EO image databases. The EO-SQ subsystem is provided with a graphic user interface (GUI) to streamline: (i) top-down knowledge transfer from-human-to-machine of an _a priori_ mental model of the 4D geospatial-temporal real-world, (ii) high-level user- and application-specific EO semantic content-based image retrieval (SCBIR) operations. Output products generated by the closed-loop EO-IU4 SQ system are expected to monotonically increase their value-added with closed-loop iterations, according to Bayesian updating where Bayesian inference is applied iteratively [122, 123]: after observing some evidence, the resulting posterior probability can be treated as a prior probability and a new posterior probability computed from new evidence. One of [PERSON]’s legacies is the notion of computational constraints required to make the typically ill-posed non-deterministic polynomial (NP)-hard problem of intelligence, encompassing vision [87], better conditioned for numerical solution [32, 33]. [PERSON]’s computational constraints reflecting properties of the world are embodied through evolution, equivalent to genotype [78], into the human visual complex system, structured as a hierarchical network of networks with feedback loops [87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98].