## 2 Materials and Methods
### Remote Sensing Data and Vegetation Types  
During field missions, we acquired more than 6000 high-resolution drone images using a DJIT(tm) Phantom IV consumer drone equipped with the standard-issue 12.4-megapixel 1/2.3''-inch CMOS sensor. Each image consisted of RGB bands with average ground sample distance (GSD) of 2.31 cm/pixel and an average flight altitude of \(\sim\)50 m above ground level, covering a total area of 15 km\({}^{2}\). From drone images, several types of aquatic vegetation were identified visually: _Potamogeton_ spp., _Creatophyllum_ spp., _Typha_ spp., _Pirmagnites_ spp., and _Nymphaea_ spp. (Figure 2a,b). In most cases, _Potamogeton_ spp. and _Creatophyllum_ spp. are associated with each other and are floating or lightly rooted and partially submerged in the water, while _Typha_ spp. and _Pirmagnites_ spp. form tall, dense stands of emergent vegetation along the edges of the water. Results from our previously published fieldwork [7] show that _Bulinus_ spp. snails (obligate intermediate hosts for S. haematobium) are strongly associated with floating and lightly rooted aquatic vegetation, specifically the abundant, partially submerged plants _Creatophyllum_ spp. and _Potamogeton_ spp. (Figure 2b), which appear as dark blue color patches on the water in RGB drone images. Drone imagery revealed sizable patches of suitable snail habitat distributed at great distances from the shore in areas inaccessible to traditional sampling methods. Results from our previous work show that measuring suitable snail habitat is a better predictor for disease risk and transmission than direct snail sampling methods [7]. Exploratory sampling from a boat also confirmed that schistome-hosting snails were indeed present in offshore floating and submerged vegetation [7].  
We used high-resolution drone imagery (Figure 2c) to validate the presence of various types of aquatic vegetation in coarser-resolution satellite images (Figure 2d). WorldView-2 multispectral satellite images were provided by the DigitalGlobe Foundation (now Maxar Technologies Inc., Westminster, CO, USA), including 36 mosaics covering a large area of our field sites in the SRB (Figure 3). These images were acquired between June and October 2016 and contain eight multispectral bands (Table 1) with a 2.0 m pixel spatial resolution. At this resolution, _Creatophyllum_ spp. and emergent vegetation are visible; however, it is not possible to distinguish _Potamogeton_ spp. from _Creatophyllum_ spp., and _Typha_ spp. from _Pirmagnites_ spp. or _Nymphaea_ spp. Therefore, we identify floating patches as the mixture of  
Figure 3: A mosaic of Maxar Technologies Inc., Westminster, CO, USA (formerly DigitalGlobe) WorldView-2 imagery of our study area, the SRB. Red markers indicate the locations of our 32 sampling sites.  
_Potamogeton_ spp. and _Creatophyllum_ spp., and emergent patches as the mixture of _Typha_ spp., _Phragmites_ spp., and _Nymphaea_ spp. (Figure 2c). About 90% of the imagery was cloudless. We selected 10 cloud-free mosaics covering our 32 water access points for deep learning training and validation. Images were gathered on 29 June 2016 (for river sites) and 4 July 2016 (for lake sites). Each selected mosaic has dimensions of 4096 \(\times\) 4096 \(\times\) 8 pixels. In order to generate our training set, we split each mosaic into 256 sub-images, each of 256 \(\times\) 256 \(\times\) 8 pixels.