## 4 Discussion  
Our results demonstrate the ability of a deep learning segmentation approach to automatically identify the aquatic vegetation associated with snail species that act as intermediate hosts for transmission for schistosomiasisomiasis to humans in the lower SRB. Using high-resolution satellite imagery, our CNN-based model outperformed a commonly used random forest approach with regard to accuracy on validation data. This new framework applies a cutting-edge AI solution that has the capacity to provide a rapid, accurate, and cost-effective risk assessment for vegetation characteristics that have previously been elusive to quantify at large scales but have recently been shown to be correlated with urinary schistosomiasisomiasis transmission hotspots in the lower SRB [7]. This approach can be potentially applied and scaled to other areas subject to endemic schistosomiasisomiasis risk, especially where the ecology of local intermediate host snails is linked to the presence and extent of floating aquatic vegetation. The deep learning workflow we presented here took considerable time for manual annotation (\(\sim\)200 human hours) on targeted vegetation and computing power to properly train the U-Net model. However, once trained and deployed, the deep learning model can analyze a satellite image and provide classification results extremely rapidly (in the scale of minutes). Our finding that the CNN (U-Net) model performed better than random forest approaches in the OOB dataset confirms previous findings that tree-based methods are less generalizable than deep learning segmentation methods [42]. In addition, adding GLCM texture layers to the U-Net model only marginally improved accuracy, which shows that deep learning does not heavily depend on the statistical features of the underlying image. Consequently, the CNN model does not require prior specification of GLCM layers by a domain expert, which makes the model less prone to bias due to researcher-specified inputs otherwise needed for conventional machine learning (e.g., random forest) approaches. In general, the benefit of deep learning is to reduce the task of developing a new feature extractor for every problem as well as to avoid human biases when solving the problem. However, the limitation with deep learning is its interpretability. Mathematically it is possible to find out which nodes of a deep neural network were activated, but it is difficult to assess what these layers of neurons were doing collectively. On the other hand, conventional machine learning algorithms such as decision trees provide crisp rules on feature selections and decode for feature importance; therefore, it is easier to interpret the reasoning behind the model. In this study, we focus more on model prediction performance and accuracy than the interpretability, so deep learning is the preferred approach in this case. The model evaluation metric selected here is pixel accuracy, which can sometimes provide misleading results when the class representation is very small within the image (\(<\)1%), as the measure will primarily be biased in reporting the model's ability to identify negative cases (i.e., when the class is not present). This is not the concern for the imagery data used in this study, as the minor class achieved a very high segmentation performance around 97% accuracy. To further analyze the distributions of false positives of the predicted \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **GLCM Features** & **Test Set Accuracy—4 Classes** & **Hold-Out Validation Set Accuracy—4 Classes** & **Test Set Accuracy—Floating Vegetation** & **Hold-Out Validation Accuracy—Floating Vegetation** \\ \hline Random forest & Added & 96.7\% & 67.8\% & 97.0\% & 68.0\% \\ \hline CNN-based & Not & 94.5\% & 82.7\% & 96.0\% & 84.0\% \\ (U-Net) & Added & 96.5\% & 83.1\% & 97.0\% & 84.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of model results on test set and hold-out validation set.