## 1 Introduction
FAO LCCS hierarchy is \"fully nested\" while alternative LC class hierarchies, such as the Coordination of Information on the Environment (CORINE) Land Cover (CLC) taxonomy [26], the U.S. Geological Survey (USGS) Land Cover Land Use (LCLU) taxonomy by [PERSON] [27], the International Global Biosphere Programme (IGBP) DISCover Data Set Land Cover Classification System [28] and the EO Image Librarian LC class legend [29], start from a Level 1 taxonomy which is already multi-class. In a hierarchical EO image understanding (EO-IU) system architecture submitted to a garbage in, garbage out (GIGO) information principle, synonym for error propagation through an information processing chain, the fully-nested two-phase FAO LCCS hierarchy makes explicit the full dependence of high-level LC class estimates, performed by any high-level (deep) LCCS-MHP data processing module, on the operational quality (in accuracy, efficiency, robustness, etc.) of lower-level LCCS modules, starting from the initial FAO LCCS-DP Level 1 vegetation/non-vegetation information layer whose relevance in thematic mapping accuracy (vice versa, in error propagation) becomes paramount for all subsequent LCCS layers. The GIGO commonsense principle, intuitive to understand in general terms as error propagation through an information processing chain, becomes neither trivial nor obvious to understand when applied to a hierarchical LC class taxonomy, starting from a FAO LCCS-DP Level 1 vegetation/non-vegetation  
Figure 1: As in [16], courtesy of the Food and Agriculture Organization (FAO of the United Nations (UN). Two-stage fully-nested FAO Land Cover Classification System (LCCS) taxonomy. The first-stage fully-nested 3-level 8-class FAO LCCS Dichotomous Phase (DP) taxonomy is general-purpose, user- and application-independent. It consists of a sorted set of three dichotomous layers: (i) vegetation versus non-vegetation, (ii) terrestrial versus aquatic, and (iii) managed versus natural or semi-natural. These three dichotomous layers deliver as output the following 8-class FAO LCCS-DP taxonomy. (A11) Cultivated and Managed Terrestrial (non-aquatic) Vegetated Areas. (A12) Natural and Semi-Natural Terrestrial Vegetation. (A23) Cultivated Aquatic or Regularly Flooded Vegetated Areas. (A24) Natural and Semi-Natural Aquatic or Regularly Flooded Vegetation. (B35) Artificial Surfaces and Associated Areas. (B36) Bare Areas. (B47) Artificial Waterbodies, Snow and Ice. (B48) Natural Waterbodies, Snow and Ice. The general-purpose user- and application-independent 3-level 8-class FAO LCCS-DP taxonomy is preliminary to a second-stage FAO LCCS Modular Hierarchical Phase (MHP) taxonomy, consisting of a battery of user- and application-specific one-class classifiers, equivalent to one-class grammars (syntactic classifiers) [19]. and featuring inter-layer semantic dependencies, equivalent to transmission lines where semantic error can propagate, from low-level (coarse) to high-level (fine) semantics [30]. On the one hand, an inherently difficult image classification scenario into vegetated/non-vegetated LC classes agrees with a minor portion of the RS literature where supervised data learning classification of EO image datasets at continental or global spatial extent into binary LC class vegetation/non-vegetation is considered very challenging [31]. On the other hand, it is at odd with the RS mainstream, where the semantic information gap from sub-symbolic EO data to multi-class LC taxonomies, where target LC classes are far deeper in semantics than the initial FAO LCCS-DP Level 1 vegetation/non-vegetation information layer, is typically filled in one conceptual stage, highly informative, but opaque (mysterious, unfathomable) in nature.