## 5 Results and Discussion  
The classification was performed following a hierarchical approach. First, a supervised k-nearest neighbor classification-KMC OpenCV considering all classes was performed. The KMC classification was carried out by normalizing the dataset due to the diversity of the input variables to make them homogeneous. The parameters adopted in the k-nearest neighbor classification-KMC (OpenCV) were a number of neighbors equal to 8, a training method classification and a type of Brute Force algorithm [41]. \begin{table}
\begin{tabular}{c c} \hline \hline
**PARAMETERS** & **INPUT SETTINGS** \\ \hline Input Raster & Orthophoto.ecw \\ Output Detected Object & Buildings and Roads \\ Model Definition: & Edited models from ESRI.dplk \\ Padding: & 32 \\ Batch\_size: & 16 \\ Threshold & 0.9 \\ Filtering threshold & 99.999 \\ Return\_boxes & False \\ Non-Maximum SuppressionOther parameters & CheckedDefault \\ \hline
**ENVIRONENTS** & **INPUT SETTINGS** \\ \hline Processing Extent & Raster extent \\ Processor Type & GPU \\ GPU id & Default \\ Cell size & Raster native GSD \\ Parallel processing & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Deep learning CNN settings in ArcGIS Pro v.2.8. It is worth noting that the water and urban areas classified with SAR and urban deep learning were joined together with the urban and water classes that were mapped with the optical data to improve these classes especially in isolated mountain villages. This improved these classes by performing a semi-automatic GIS procedure. During this joining phase, a minimum mapping unit (mmu) of 100 m was considered. Therefore, only the pixels that have this mmu were mapped as urban while the other pixels that did not intersect with urban (both SAR/deep learning and optic multispectral) with less than 100 m were considered as classified by the optical data. Since woody crops were particularly complex to discriminate (hereinafter called WC), performing only a KMC classification due to the single multispectral composite input dataset did not permit us to consider the whole phenological active season. A hierarchical classification approach was then implemented to try to overcome this issue. In particular, the developed EO service foresaw the first classification (considering the S2 main input dataset) with all the classes according to the new EAGLE land cover legend and a subsequent one with only WC class. In the end, the two classifications were subjected to a mosaicking process by first applying an overlap for WC. Then, the doubtful areas were corrected manually by photo-interpretation of composite PlanetScope imagery. Regarding the WC class, a supervised minimum distance classification (MDC) was performed, including the following input datasets: a yearly cloud-shadow masked NDVI stack filtered (Savitzky-Golay) [73; 74; 75] and regularized at 10 days times-steps [76] on the GEE, and an annual stack of the NDRE index (normalized difference red-edge index for agriculture) following the same procedure of the NDVI stack [77]:  
\[\mathrm{NDRE}=\frac{\mathrm{NIR-RE}}{\mathrm{NIR+RE}}\]  
NDVI composite Entropy [32; 78]  
\[\mathrm{H_{NDVI}}=-\sum_{\mathrm{i=0}}^{\mathrm{N-1}}\sum_{\mathrm{j=0}}^{ \mathrm{N-1}}\mathrm{NDVI_{ij}}\log\left(\mathrm{NDVI_{ij}}\right)\]  
where \(\mathrm{NDVI_{ij}}\) is the NDVI value at the i-th row and the j-th column in the local square window, measuring N pixels. For this study, a kernel window size of 10 \(\times\) 10 pixels was adopted. Using [PERSON]'s Q Diversity index on the S2 NDVI composite [79], [PERSON]'s Q is calculated using half the squared Euclidean distance. Therefore, the resulting index is [80]:  
\[\mathrm{Q}=\sum\mathrm{d_{\mathrm{ii}}*P_{i}*P_{j}}\]  
where \(\mathrm{p_{i}}\) and \(\mathrm{p_{j}}\) are the proportion of the area for each category per the rows and columns in the pairwise distance \(\mathrm{d_{\mathrm{ii}}}\). The pattern analysis of the S2 NDVI composite used the following parameters: (a) dominance, (b) diversity, (c) relative richness and (d) fragmentation [81].