## 1 Introduction
[PERSON]’s computational constraints are Bayesian priors in a Bayesian inference approach to vision [76, 122], where ever-varying sensations (sensory data) are transformed into stable percepts (concepts) about the world in a world model [23], to perform successfully in the world [18]. Based on scientific literature [13; 14; 15; 67; 82; 83; 124], a CV \(\supset\) EO-IU system is defined in operating mode if and only if it scores \"high\" in every index of a minimally dependent and maximally informative (mDMI) set of EO outcome and process (OP) quantitative quality indicators (Q\({}^{2}\)Is), to be community-agreed upon for use by members of the RS community, in agreement with the GEO-CEOS QA4 EO _Cal/Val_ guidelines [3]. A proposed instantiation of an mDMI set of EO OP-Q\({}^{2}\)Is includes the following. 1. Degree of automation, inversely related to human-machine interaction, e.g., inversely related to the number of system's free-parameters to be user-defined based on heuristics. 2. Effectiveness, e.g., thematic mapping accuracy. 3. Efficiency in computation time and in run-time memory occupation. 4. Robustness (vice versa, sensitivity) to changes in input data. 5. Robustness to changes in input parameters to be user-defined. 6. Scalability to changes in user requirements and in sensor specifications. 7. Timeliness from data acquisition to information product generation. 8. Costs in manpower and computer power. 9. Value, e.g., semantic value of output products, economic value of output services, etc. According to the Pareto formal analysis of multi-objective optimization problems, optimization of an mDMI set of OP-Q\({}^{2}\)Is is an inherently-ill posed problem in the Hadamard sense [117], where many Pareto optimal solutions lying on the Pareto efficient frontier can be considered equally good [125]. Any EO-IU system solution lying on the Pareto efficient frontier can be considered in operating mode, therefore suitable to cope with the five Vs of spatial-temporal EO _big data_, namely, volume, variety, veracity, velocity and value [4]. In the multidisciplinary domain of cognitive science (see Figure 2), _vision_ is synonym for _scene-from-image reconstruction and understanding_[23], see Figure 6. Encompassing both biological vision and CV, _vision_ is a cognitive (_information-as-data-interpretation_) problem [18], very difficult to solve because: (i) non-deterministic polynomial (NP)-hard in computational complexity [87; 116], (ii) inherently ill-posed in the Hadamard sense [117], i.e., vision admits no solution, multiple solutions or, if the solution exists, the solution's behavior changes continuously with the initial conditions [23; 75]. _Vision_ is inherently ill-posed because affected by: (I) a 4D-to-2D data dimensionality reduction, from the geospatial-temporal scene-domain to the (2D, planar) image-domain, e.g., responsible of occlusion phenomena, and (II) a semantic information gap, from ever-varying sub-symbolic sensory data (sensations) in the physical world to stable symbolic percepts in the mental model of the physical world (modeled world, world ontology, real-world model) [12; 18; 19; 20; 21; 22; 23; 24], see Figure 6. Since it is inherently ill-posed, _vision_ requires _a priori_ knowledge in addition to sensory data to become better posed for numerical solution [32; 33]. For example, in inherently ill-posed CV systems, a valuable source of _a priori_ knowledge is reverse engineering primate visual perception [87; 88; 89; 90; 91; 92; 93], so that a CV system is constrained to include a computational model of human vision [13; 76; 88; 89; 100; 101; 102; 103; 104], i.e., 'Human vision \(\rightarrow\) CV', see Figure 2. According to cognitive science, AI \(\supset\) CV is not a problem in statistics [125], which is tantamount to saying _there is no_ (qualitative, equivocal, nominal) _semantics in_ (quantitative, unequivocal, numeric) _sensory data_[13].