Floods are one of the most devastating and costly natural disasters, posing a significant threat to human life and property, and necessitating systematic and timely response to flood risks. While most floods cannot be prevented, they can be detected, and a quick response can greatly reduce the consequences. Recent advancements in artificial intelligence, computing power, and earth observation data availability has enabled researchers to use computer vision and satellite/aerial imagery to help assess ground conditions and decision-makers’ prioritization of response efforts. This paper investigates different algorithmic design decisions to determine best flood line detection performance. We also investigated the value of adding non-imagery proxy data used for flood prediction into a computer vision pipeline, which includes the combination of Height Above Nearest Drainage (HAND)-based inundation map data and aerial imagery to train a semantic segmentation convolutional neural network. In our experiments, we trained several U-Net shaped fully convolutional neural networks using aerial imagery of hurricane Harvey retrieved from the National Oceanic and Atmospheric Administration (NOAA) repositories, and rasterized HAND map data retrieved from The Texas Advanced Computing Center (TACC). The paper contributes by showcasing the results of combining both a hydrologic and computer vision method for flood detection.