Flooding in urban areas, especially in low-income or disadvantaged communities, poses a serious problem to drivers. While techniques exist to map and predict flooding events, a knowledge gap exists in accurate mapping and prediction of urban flooding. It is important to have an understanding of how much flooding a region may experience given a certain weather event so that drivers may preemptively avoid flooded areas. This paper synthesizes several approaches to build an understanding of the spatial extent of urban flooding in the frequently flooded parts of San Diego, California. First, flooding reported during major storms was used as validation data for a Generalized Linear Regression model to create a map of flood risk. Then, a Support Vector Machine model was used to extract areas of possible flooding from a satellite image. Finally, model performance was compared. Each model provided robust and meaningful results, with the Generalized Linear Model indicating which areas of the city are most at risk for flooding and the image classification Support Vector Machine model successfully identifying water bodies during both dry and wet conditions.