## 1 Introduction
This one-stage mapping from sub-symbolic sensory data to high-level (symbolic) concepts is typically implemented as a supervised data learning classification stage [32; 33], e.g., a support vector machine, random forest or deep convolutional neural network (DCNN) [34; 35; 36; 37; 38], which is equivalent to a black box learned from supervised (labeled) data based on heuristics (e.g., architectural metaparameters are typically user-defined by trial-and-error) [30], whose opacity contradicts the well-known engineering principles of modularity, regularity and hierarchy typical of scalable systems [39]. In addition, inductive algorithms, capable of learning from either supervised (labeled) or unsupervised (unlabeled) data, are inherently semi-automatic and site-specific [2]. In general, \"No Free Lunch\" theorems have shown that inductive learning-from-data algorithms cannot be universally good [40; 41]. Over land surfaces of the Earth, the global cloud cover is approximately 66% [42]. In the ESA EO Level 2 product definition, cloud and cloud-shadow quality layer requirements specification accounts for a well-known prerequisite of clear-sky multi-temporal EO image compositing and understanding (classification) solutions proposed by the RS community, where accurate masking of cloud and cloud-shadow phenomena is considered necessary, but not sufficient pre-condition [12; 13; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. Intuitively, in single-date and multi-temporal MS image analysis, cloud and cloud-shadow preliminary detection is a relevant problem, because unflagged cloud and cloud-shadow phenomena may be mapped onto erroneous LC classes or false LC change (LCC) occurrences. It is noteworthy that joint (combined) cloud and cloud-shadow detection is a typical example of physical model-based cause-effect relationship, expected to be very difficult to solve by inductive machine learning-from-data algorithms, such as increasingly popular DCNNs [34], with special regard to DCNNs designed and trained end-to-end for semantic segmentation [37] and instance segmentation [38] tasks, whereas DCNNs trained for object detection, such as [36], where image-objects are localized with bounding boxes and categorized into one-of-many categories, are inapplicable to the cloud/cloud-shadow instance segmentation problem of interest. In general, inductive supervised data learning algorithms are capable of learning complex correlations between input and output features, but unsuitable for inherent representations of causality [30; 64], in agreement with the well-known dictum that correlation does not imply causation and vice versa [13; 19; 30; 64; 65]. In the last decade, many different cloud/cloud-shadow detection algorithms have been presented in the RS literature to run either on a single-date MS image or on an MS image time-series, typically acquired by either one EO spaceborne/airborne MS imaging sensor or a single family (e.g., Landsat) of MS imaging sensors [12; 13; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60]. To be accomplished in operating mode at the ground segment (midstream) by EO data providers in support of the downstream sector within a \"seamless chain of innovation\" needed for a new era of Space Economy 4.0 [66], systematic radiometric _Cal_ of multi-source multi-angular multi-temporal MS _big image data cubes_[1; 3; 67; 68; 69], encompassing either single-date or multi-temporal cloud and cloud-shadow detection as a necessary-but-not-sufficient pre-condition, is regarded by the RS community as an open problem to date [61; 62; 63]. In agreement with the GEO-CEOS QA4 EO _Cal_/_Val_ requirements [3], this work presents an innovative AutoCloud+ computer vision (CV) software system for cloud and cloud-shadow quality layer detection.