## 4 Conclusions  
For the sake of readability this paper is divided in two. To highlight the importance of a \"universal\" AutoCloud+ CV software for systematic cloud and cloud-shadow quality layers detection in multi-sensor, multi-angular and multi-temporal EO _big data cubes_, the present Part 1 discloses AutoCloud+ in a broad context of systematic ESA EO Level 2 product generation at the ground segment [11; 12] or space segment [84; 85; 144; 163], whose necessary-but-not-sufficient pre-condition is cloud/cloud-shadow quality layers detection in operating mode (refer to Section 2). The subsequent Part 2 (see Supplementary Materials) copes with the AutoCloud+ CV software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results. Original contributions and main conclusions of the present Part 1 are summarized hereafter. Conceptual in nature and pertaining to the interdisciplinary domain of cognitive science [18; 77; 78; 81; 79; 80; 81], see Figure 2, our working hypothesis is the first original contribution of this RTD study, see Figure 4 and Equation (1). Working hypothesis (1) postulates that systematic ESA EO Level 2 product generation in operating mode [11; 12], whose _part-of_ is cloud and cloud-shadow quality layer detection, is necessary-butnot-sufficient pre-condition for multi-sensor multi-temporal and multi-angular EO _big data cube_ analytics as _part-of_ the GEO-CEOS visionary goal of a GEOSS [5], never accomplished to date by the RS community. The general notion of GEOSS encompasses open sub-problems, such as semantic content-based image retrieval (SCBIR) + semantics-enabled information/knowledge discovery (SEIKD) = artificial general intelligence (AI) for Data and Information Access Services (AI4 DIAS) at the ground segment. Dependence relationship (1) means that the GEOSS open problem, together with its still-unsolved (open) sub-problems of SCBIR and SEIKD, cannot be accomplished until the necessary-but-not-sufficient pre-condition of CV \(\supset\) EO image understanding (EO-IU) in operating mode, specifically, systematic ESA EO Level 2 product generation featuring cloud/cloud-shadow quality layers detection, is fulfilled in advance. The second original contribution of the present Part 1 is both conceptual and pragmatic in the definition of RS best practices, which is the focus of efforts made by intergovernmental organizations such as GEO and CEOS. In more detail, the ESA EO Level 2 product definition is regarded as baseline information primitive (unit of EO data-derived information) eligible for use as \"augmented\" (enhanced) EO Analysis Ready Data (ARD) format, to be adopted as horizontal policy for standardization purposes by a \"seamless innovation chain\" needed for a new era of Space 4.0 [66], see Figure 8. Such an \"augmented\" EO ARD definition is more restrictive (in terms of output product requirements specification), more informative (in terms of physical and conceptual/semantic quality of numeric and categorical output products, respectively), but more difficult to be inferred from EO sensory data than existing U.S. Landsat ARD [139; 140; 141; 142; 143] and CEOS ARD for Land (CARD4L) format definitions [138]. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2220-9964/7/12/457/s1](http://www.mdpi.com/2220-9964/7/12/457/s1)]([http://www.mdpi.com/2220-9964/7/12/457/s1](http://www.mdpi.com/2220-9964/7/12/457/s1)). **Author Contributions:** Conceptualization, A.B. and D.T. ; Methodology, A.B. and D.T. ; Software, A.B. and D.T. ; Validation, D.T. ; Formal Analysis, A.B. and D.T. ; Investigation, A.B. and D.T. ; Resources, A.B. and D.T. ; Data Curation, D.T. ; Writing--Original Draft Preparation, A.B. ; Writing--Review and Editing, A.B. and D.T. ; Visualization, A.B. and D.T. ; Supervision, D.T. ; Project Administration, D.T. ; Funding Acquisition, D.T.