## 1 Introduction
Intuitively, EC-DIAS is affected by the DRIP syndrome because it is provided with no CV system in operating mode as inference engine, capable of transforming geospatial-temporal EO _big data_, characterized by the five Vs of volume, variety, veracity, velocity and value [4], into VAPS, starting from semantic information products, such as the ESA EO Level 2 SCM baseline product. Sketched in Figure 5, AI4 DIAS complies with the [PERSON]'s intuition that \"vision goes symbolic almost immediately without loss of information\" [76] (p. 343). Second, in our understanding the ESA EO Level 2 product definition is the new standard of EO Analysis Ready Data (ARD) format. This ARD definition is in contrast with the Committee on Earth Observation Satellites (CEOS) ARD for Land (CARD4L) product definition [138], where atmospheric effect removal is required exclusively, i.e., adjacency topographic and BRDF effect corrections are oversighted, and no EO data-derived SCM is expected as additional output product, which includes quality layers cloud and cloud-shadow. It is also in contrast with the U.S. Landsat ARD format [139; 140; 141; 142; 143], where atmospheric effect removal is required exclusively, i.e., adjacency topographic and BRDF effect corrections are omitted, and where quality layers cloud and cloud-shadow are required, but no EO data-derived required, but no EO data-derived SCM is generated as additional output product. In practice, the CARD4L and U.S. Landsat ARD definitions are _part-of_ the ESA EO Level 2 product definition. The latter encapsulates the former. When the latter is accomplished, so is the former; however, the vice versa does not hold. Figure 8: Definitions adopted in the notion of Space Economy 4.0: space segment, ground segment for +mission control> = upstream, ground segment for +user support> = midstream (infrastructures and services), downstream utility of space technology [66] (pp. 6, 57). Cable of transforming quantitative (unequivocal) _big data_ into qualitative (equivocal) data-derived value-adding information and knowledge, AI technologies should be applied as early as possible to the “seamless innovation chain” needed for a new era of Space 4.0, starting from AI4 Space applications at the space segment, which include the notion of future intelligent EO satellites (FIEOS) [144; 145], and AI4 DIAS applications at midstream, such as systematic ESA EO Level 2 product generation, considered synonym for Analysis Ready Data (ARD) eligible for use at downstream. Third, we consider ESA EO Level 2 product generation a horizontal policy for background developments in support of a new era of Space Economy 4.0 [66], see Figure 8. In the notion of Space 4.0, global value chains will require both vertical and horizontal policies. Vertical policies are more directional and 'active', focusing on directing change, often through mission-oriented policies that require the active creation and shaping of markets. Horizontal policies are more focused on the background conditions necessary for innovation, correcting for different types of market and system failures [66]. Fourth, in an \"old\" mission-oriented (vertical) space economy [66], the ground segment is typically divided into upstream and midstream, which are defined as the portion of ground segment for mission support and user support respectively, see Figure 6.0 ESA expects systematic ESA EO Level 2 product generation to be accomplished by EO data providers at midstream. Actually, systematic ESA EO Level 2 product generation should occur as early as possible in the information processing chain, e.g., in the space segment preliminary to the ground segment, in compliance with the [PERSON]'s intuition that \"vision goes symbolic almost immediatel without loss of information\" [76] (p. 343). Hence, in Figure 4, ESA EO Level 2 product generation becomes synonym for AI applications for the space segment, AI4 Space, where AI \(\supset\) CV. AI4 Space comes before the application of AI techniques to the ground segment, specifically, AI4 DIAS.