# Multi-Source Satellite Imagery and Machine Learning
## 7 Discussion
The model's ability to effectively identify complex geological formations fully justifies the use of advanced machine learning methods, such as Random Forest, in contexts where simpler techniques fail to capture geological complexity. The results for trachytes and basalts illustrate the potential of these approaches for more demanding geological applications and highlight their value beyond the simplest cases. The study also emphasizes the varying impact of different parameter sets on model performance. Geographic parameters (DGPs) consistently yield solid results, particularly for classes like alluvium, trachytes, basalts, and pyroclastics, where spatial context plays a critical role. Random Forest and XGBoost exhibit competitive performance, with Random Forest achieving 51.53% accuracy and an MCC of 37.39%, while Gradient Boosting and XGBoost also perform well. OLI spectrometric parameters (DOLIPs) significantly influence model performance due to their spectral richness. Random Forest again leads with 52.59% accuracy and an MCC of 30.32%, followed by XGBoost with 43.34% accuracy and 30.09% MCC. These findings highlight the strength of OLI data in capturing key geological characteristics, making them a strategic choice for geospatial and spectral classification tasks. Principal component analysis (PCA) parameters (DPCAPs) reduce data dimensionality while retaining essential information, improving execution times--Random Forest processes this data in just 2.60 ms. However, PCA data yield lower accuracy (41.98%) and MCC (28.23%) compared to other datasets. PCA proves useful in specific cases, such as granite and trachyte detection, where its dimensionality reduction aids in handling complex data, albeit sometimes at the expense of accuracy. Combining different datasets generally enhances model performance, though the impact depends on the combination. The DGP-DPCAP combination yielded the highest accuracy (55.29%) and MCC (45.12%) with Random Forest. In contrast, combining DOLIP and DPCAP resulted in less optimal outcomes, suggesting that excessive data complexity can hinder model effectiveness. For example, the DPCAP-DOLIP combination with Random Forest produced lower accuracy (45.05%) and MCC (31.81%) than other combinations. This study demonstrates that geographic (DGP) and OLI spectrometric parameters (DOLIPs) are most effective in boosting classification performance, particularly when usingRandom Forest. While PCA parameters (DPCAPs) reduce dimensionality and execution time, they offer lower accuracy and MCC than other datasets. Finally, it is important to contextualize the significance of reduced computation time in geological studies. While algorithmic efficiency is often viewed as a benefit, the time saved--typically measured in seconds or minutes--is negligible compared to the extensive time required for ground surveys and laboratory analyses, which can span weeks or months. Therefore, while computational efficiency is valuable, its impact is secondary to the time-intensive processes involved in geological validation. The true value of advanced algorithms lies in their ability to process large, heterogeneous datasets and extract complex patterns, rather than simply minimizing execution time. The results align with [PERSON] et al. [25], who demonstrated the effectiveness of multi-source approaches in geological mapping. Although Random Forest performed well in this study, its accuracy remains below the 96% reported by [PERSON] et al. using convolutional neural networks (CNNs), underscoring the potential benefits of exploring deep learning architectures for enhanced classification. Similarly, the performance of XGBoost in certain configurations corroborates the findings of [PERSON] et al. [13], who highlighted the algorithm's robustness in handling multisource data within complex geological contexts. A major challenge identified in this study is the imbalance of geological classes within the dataset, with certain formations being overrepresented.