## 5 Results and Discussion
Then, the KMC data were mosaicked using as first overlap onto the MDC to refine only the WC class. The same was done considering urban and water masks mapped using the S1 data. As a last step, a simple filter was performed using a radius greater than 20 m. Furthermore, in the final classification, deep learning features considered in the urban and anthropic areas were included and the confusion matrix computed. The scalable Earth Observation service to map land cover in geomorphological complex areas beyond the Dynamic World developed for the Aosta Valley region are reported in Figure 4.  
The parameters reported in the confusion matrix [82] were: overall accuracy, errors of commission and omission, user and producer accuracy, sum users and sum producers and unclassified pixels (in this case each pixel were classified). The sum user indicates the number of pixels for each class in each row while the sum producer represents the number of pixels for each class in each column. The overall accuracy is calculated by summing the number of the correctly classified values and dividing it by the total number of values. Finally, the kappa coefficient measures the agreement between the classification and the truth values. It is worth noting that a comparison with traditional methods was followed to prove the real effectiveness of the suggested approach and the developed EO services. Therefore, the k-coefficients were computed per each approach. A traditional approach that adopted only the optic multispectral data was followed by performing a unique one-shot classification using KMC. A combined approach adopted a single KMC supervised classification with the optical data, considering all the classes and the two classifications involving only urban and water with random forest and SAT, respectively, including SAR data. Finally, the mixed hierarchical approach with the two optical supervised classifications (KMC + MD), the two SAR classifications (for urban and water respectively) and deep learning was described in this work. The hierarchical approach improved the quality of the obtained classifications, as shown in Table 8. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Overall Accuracy** & **K-Coefficient** \\ \hline Traditional approach & 88\% & 0.88 \\ Combined approach & 89\% & 0.89 \\ Mixed Hierarchical approach & 97\% & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracies. Figure 4: Workflow processing to process the scalable Earth Observation service to map land cover in geomorphological complex areas. The developed EO service represented a valuable to map land cover at high temporal and spatial resolutions. The combined application of S1 (only for a couple of classes) and S2 EO data coupling deep learning techniques boosted the classification of land cover components in geomorphological complex areas such as the Alps. It is worth noting that the S1 data were adopted only to better map urban and water areas due to misleading classifications that may occur due to the physical limitations of SAR in mountainous areas. Moreover, the S1 processing especially related to interferometry required high performance computing machines and would not permit a rapid land cover mapping. The developed EO service was considered to be scalable to other morphological complex realities, in particular the mountainous areas. The realized EO local service with free EO data and open-source tools, except from ESRI ArcGIS (that can be replaced by QGIS and Python script for deep learning), represented a possible workflow to perform ongoing territorial planning and management. The present EO service led to an important technology transfer in the Aosta Valley territory answering various requests at different levels (European, national and local). This EO service will streamline the implementation of local policies concerning land cover monitoring and assessment.