Wildfires have become a major ecological issue impacting humans and the environment alike. To prevent wildfires from starting in the first place, better spatial prediction tools are needed to assess wildfire risk between locations to allow better specialization of firefighting agencies in performing preventative measures. Current methods of predicting spatial wildfire risk rely on satellite imagery, which is significantly limited in terms of spatial and temporal resolution. The use of unmanned aerial vehicles (UAVs) for assessing wildfire risk has significant advantages over satellites due to their much higher spatial and temporal resolution. This project sought to develop a novel deep learning model to predict wildfire risk through aerial imaging specialized for deployment on UAVs. Convolutional neural network architectures were investigated due to their ability to extract implicit spatial features from data. Google Earth Engine was used to assemble a geospatial dataset containing patches of vegetation burnability indices each mapped to a wildfire risk index based on historical fire prevalence. The results for the model show extremely high performance across all metrics evaluated. From this proof of concept research, UAV-based prediction offers a promising prospective solution to the drawbacks of satellite imaging in remote sensing of wildfire risk.