## 1 Introduction
In greater detail, the capability to detect and quantify change/no-change in terms of either qualitative (nominal, categorical) Earth surface variables, such as LC classes belonging to a finite and discrete LC class taxonomy (legend), or quantitative (numeric) Earth surface variables, such as biophysical variables, e.g., leaf area index (LAI), biomass, etc., depends on the radiometric _Cal_ of EO sensory data, equivalent to non-negative dimensionless DNs \(\geq 0\), provided with no physical meaning and typically affected at sensor-level by ever-varying atmospheric conditions, solar illumination conditions, spaceborne/airborne viewing geometries and Earth surface topography, into a physical variable provided with a community-agreed radiometric unit of measure, such as TOARF, SURF or surface albedo values in range 0.0-1.0 [1; 2; 3]. Solar illumination conditions are typically parameterized by metadata _Cal_ parameters, such as image acquisition time, solar exo-atmospheric irradiance, solar zenith angle and solar azimuth angle, see Figure 3. Sensor viewing characteristics are typical metadata _Cal_ parameters, such as sensor zenith angle and sensor azimuth angle, see Figure 3. Atmospheric conditions are described by categorical variables, such as aerosol type, haze, cloud and cloud-shadow, and by numeric variables, such as water vapor, temperature and aerosol optical thickness (AOT) [68; 69]. Finally, Earth surface geometries must be inferred from ancillary data, such as a digital elevation model (DEM), in combination with solar and viewing conditions [107], see Figure 3.  
Adopted by different scientific disciplines, such as inductive machine learning-from-data [32; 33], AI as _superset-of_ CV [77], i.e., AI \(\supset\) CV (see Figure 2), and RS [2; 13], popular synonyms for _deductive inference_ are top-down inference, prior knowledge-based inference, learning-by-rule inference and physical model-based inference. Synonyms for _inductive inference_ are bottom-up inference, learning-from-data inference, learning-from-examples inference and statistical model-based inference [82; 83]. On the one hand, non-calibrated sensory data, provided with no physical meaning, can be investigated by statistical data models and inductive inference algorithms, exclusively. On the other hand, although they do not require physical variables as input, statistical data models and inductive learning-from-data algorithms can benefit from input data _Cal_ in terms of augmented robustness to changes in the input data set acquired through time, space and sensors. In contrast, radiometrically calibrated data, provided with a physical meaning, can be interpreted by either inductive, deductive (physical model-based) or hybrid (combined deductive and inductive) inference algorithms. Although it is considered a well-known \"prerequisite for physical model-based (and hybrid) analysis of airborne and satellite sensor measurements in the optical domain\" [1; 13; 67; 68; 69], EO data _Cal_ is largely neglected in the RS common practice. For example, in major portions of the RS literature, including the GEOBIA sub-domain of GIScience [9; 10], no reference to radiometric _Cal_ issues is found. This lack of input EO data _Cal_ requirements proves that, to date, EO image analytics mainly consists of inductive learning-from-data algorithms, starting from scratch because no _a priori_ physical knowledge is exploited in addition to data. This is in contrast with biological cognitive systems, where \"there is never an absolute beginning\" [108], because _a priori_ genotype provides initial conditions (that reflect properties of the world, embodied through evolution, based on evolutionary experience) to learning-from-examples phenotype, according to a hybrid inference paradigm, where phenotype explores the neighborhood of genotype in a solution space [13; 78]. Hybrid inference combines deductive and inductive inference to take advantage of each and overcome their shortcomings [2]. Inductive inference is typically semi-automatic and site-specific [2].