## 4 Discussion
floating vegetation (see the black color pixels in the inference maps; Figure 7c,d), we found that most false positives were located along the edge of floating vegetation patches as well as the boundary with other vegetation, which indicates that more labeled pixels on the vegetation patches are required to improve the pixel accuracy of the model. However, the purpose of this study is to show the capability of generating rapid prediction and generalized mappings of the floating vegetation for the larger area instead of providing small details on how floating vegetation patches are distributed. The latter is more important in analyzing the seasonal changes in the floating vegetation and how it correlates with schistosomiasisomassis transmission. This will be the focus of our follow-up studies. In this study, the drone imagery was used to validate the presence and identity of aquatic vegetation on satellite imagery that was captured in a similar time period (less than a few months apart). For future studies, the drone imagery could have greater utilization in training deep learning models, e.g., for image super-resolution [43] as well as resolution enhancement [44] for satellite imagery in an adversarial training setting [45], as long as the satellite data is captured at the same time and place, ideally less than a few weeks apart, which would require precise planning and coordination of satellite image capture and drone operation activities. The opportunity to couple synchronous drone and satellite imagery becomes more and more feasible as more high-resolution satellites circle the Earth and create Earth observation data at a faster tempo. For example, the Planet satellite constellation now provides high-resolution satellite imagery with a global extent on a daily basis ([[https://www.planet.com](https://www.planet.com)]([https://www.planet.com](https://www.planet.com)), accessed on: 8 January 2022). The segmentation algorithm and resultant aquatic vegetation inference maps presented here demonstrate proof of principle for the utility of a deep learning approach to enable production of aquatic vegetation maps with potential public health relevance for schistosomiasisomassis transmission control. So far, the model has only been trained and validated over a relatively large spatial extent in the lower SRB, one of the most important hotspots for schistosomiasisomassis transmission in the world, but there is strong potential to apply this model to larger spatial extents using remote sensing images from different river and lake systems to build a more generalized model. The next step in the development of this approach is to validate the predictive power of vegetation maps produced by this method for actual snail and human prevalence or incidence data. If indeed vegetation maps derived through the integration of high-definition satellite imagery with deep learning prove accurate in predicting snail presence or abundance and/or human infection risk across large scales, then this tool has the potential to provide unprecedented precision mapping of snail suitable habitat to efficiently target limited resources to control disease in the high-transmission areas. Future studies should thus focus on validating the ability of these vegetation inference maps to correctly predict human disease transmission in the SRB and other regions of Africa where there is endemic schistosomiasisomassis transmission in a similar ecological context.