## 1 Introduction
To be eligible for systematic ESA EO Level 2 product generation at the ground segment [43; 67; 70], AutoCloud+ must overcome conceptual (structural) limitations and well-known failure modes of standard cloud and cloud-shadow detection algorithms [44; 61; 62; 63; 64; 65; 66; 67; 68; 69],such as the single-date multi-sensor Function of Mask (FMask) open source algorithm [58, 59], the single-date single-sensor ESA Sen2 Cor software toolbox [11, 12, 44], to be run free-of-cost on the user side, and the multi-date Multisensor Atmospheric Correction and Cloud Screening (MACCS)-Atmospheric/Topographic Correction (ATCOR) Joint Algorithm (MAJA) developed and run by the Centre national d'etudes spatiales (CNES)/Centre d'Etudes Spatiales de la Biosphere (CESBIO)/Deutsches Zentrum fur Luft- und Raumfahrt (German Aerospace Center, DLR) [46, 47, 48], which incorporates capabilities of the ATCOR commercial software toolbox [71, 72, 73, 74]. A synonym for inherently ill-posed scene-from-image reconstruction and understanding [13, 23, 75, 76], _vision_ is a cognitive (_information-as-data-interpretation_) process [18], encompassing both biological vision and CV, where CV is _subset-of_ artificial general intelligence (AI) [77, 78, 79, 80, 81], i.e., AI \(\supset\) CV, in the multi-disciplinary domain of cognitive science [18, 77, 78, 79, 80, 81], see Figure 2. In _vision_, spatial information dominates color information [23]. This unquestionable true fact is familiar to all human beings wearing sunglasses: in perceptual terms, human panchromatic and chromatic visions are nearly as effective [13]. Starting from this simple, yet not trivial, observation about human visual perception, in order to outperform standard CV software toolboxes in operating mode for cloud and cloud-shadow detection in EO _big data cubes_, such as the single-date sensor-specific ESA Sen2 Cor [11, 12, 44] and the multi-date multi-sensor CESBIO/CNES/DLR MAJA software [46, 47, 48], degrees of novelties of an innovative \"universal\" AutoCloud+ CV software system are expected to encompass the Marr five levels of understanding of an information processing system, specifically [13, 19, 76, 82, 83]:  
* outcome and process requirements specification, including computational complexity estimation,
* information/knowledge representation,
* system design (architecture),
* algorithm, and
* implementation. Among these five levels, the three more abstract ones, namely, outcome and process requirements specification, information/knowledge representation and system design, are typically considered the linchpin of success of an information processing system, rather than algorithm and implementation [13, 19, 76, 82, 83]. To be considered \"universal\" and in operating mode, the AutoCloud+ software system's outcome and process requirements were specified as follows. 1. \"Fully automated\", i.e., no human-machine interaction and no labeled data set for supervised inductive learning-from-data are required by the system to run, which reduces timeliness, which is the time span from EO data acquisition to EO data-derived VAPS generation, as well as costs in manpower (e.g., to collect training data) and computer power (no training time is required). 2. Near real-time, e.g., computational complexity increases linearly with image size. 3. Robust to changes in input sensory data acquired across space, time and sensors. 4. Scalable to changes in MS imaging sensor's spatial and spectral resolution specifications. 5. Last but not least, AutoCloud+ must be eligible for use in multi-sensor, multi-temporal and multi-angular EO _big data cubes_, either radiometrically uncalibrated, such as MS images typically acquired without radiometric _Cal_ metadata files by small satellites [84] or small unmanned aerial vehicles (UAVs) [85], or radiometrically calibrated into TOARF, SURF or surface albedo values in agreement with the GEO-CEOS QA4 EO _Cal_/_Val_ requirements [3]. For the sake of readability this paper is divided in two.