## 2 Data and Methods  
The key element of the database is remote sensing data combined with digital models of the Earth's surface elevations, including, in particular, aerophotos and satellite images of the terrain. At the middle scale (1:50,000-1:100,000), remote sensing data were downloaded from open access USGS website EarthExplorer; at the large scale, remote sensing images were produced by an unmanned aerial vehicle survey based on the GeoScan technology. A detailed digital elevation model was developed in two ways: the first one (main) included digitizing originally analogous topographic maps available through a licensed Android application, ATLOGIS Geoinformatics; the second way was used for the mentioned digitizing update--it was based on stereophotography and laser sensing during the unmanned aerial vehicle survey. Remote sensing data combined with digital elevation models are defined as scaled-down models of the terrain, where geological and structural anomalies are encoded in combinations of landscape components, and these combinations are encoded in the set of half-tone photo anomalies. Geological heterogeneities, which are not explicitly observed at the surface due to masking by soil and vegetation cover, intense weathering processes and anthropogenic influences, are clearly manifested in remote sensing data. Physically, this is explained by the evolutionary process of landscape formation within the considered territory, where all landscape components functionally depend on geological heterogeneities. At the stage of expert decoding [26], the lineament field is reconstructed both with remote sensing data and DME for reliable verification of results. The lineament as a linearized (straightened) landscape element is traced through heterogeneous forms of half-tone anomalies in remote sensing data and heterogeneous forms of relief in order to map the elements of fracture tectonics, obtaining the latest activation [25; 27]. Expert decoding is based on visual estimations and manual, little-automated operations with one or the set of most contrasting spectrum bands. Here, the contrast is traditionally defined as parameter  
\[K=(B_{2}-B_{1})/B_{2},\ 0<K<1,\]computed by the values of the optical density field between pairs of neighboring objects and averaged over the entire considered area. Scalar fields of parameters derived from the initial scalar field by means of application of differential operators and estimation of exponential parameters are a hint in monitoring the results of expert decoding. The calculation of shadow forms of the geofield surface \(f(x,y)\), when this surface is illuminated by a homocentric source [28], can be defined as the simplest transform of this type. If \(\beta\) is the angle formed by the incident ray and the horizon plane, \(\gamma\) is the angle between the conditional direction to the north and the projection of the incident ray onto the horizontal plane, counted clockwise, and  
\[\phi=arctg\left(\left(\frac{\partial f}{\partial x}\right)^{2}+\left(\frac{ \partial f}{\partial y}\right)^{2}\right)\]  
is the slope angle of the virtual surface of the geofield, and  
\[\varphi=\left(arctg\left(\frac{\partial f}{\partial y}-\frac{\partial f}{ \partial x}\right)\right)^{2}\]  
is the angular sector of the view, where  
\[\frac{\partial f}{\partial x}=\frac{(f_{31}+2f_{32}+f_{33})-(f_{11}+2f_{12}+ f_{13})}{8\cdot K_{S}},\]  
\[\frac{\partial f}{\partial y}=\frac{(f_{13}+2f_{23}+f_{33})-(f_{11}+2f_{21}+f_ {31})}{8\cdot K_{S}},\]  
\(K_{S}=9\) is the number of matrix elements composed by values of the geofield in the sliding window:  
\[3\;\times\;3\;\left( \begin{array}{ccc}f_{11}&f_{12}&f_{13}\\ f_{21}&f_{22}&f_{23}\\ f_{31}&f_{32}&f_{33}\end{array}