A significant proportion of single-vehicle fatalities in the United States occur in rural areas. One critical factor in crash severity models is the slope of roadside terrain (side slope). Roadway geometry databases often lack reliable side slope data, but there is currently a nationwide effort to collect elevation data through aerial light detection and ranging (LiDAR) scans. In this preliminary study, we develop a software tool for extracting roadway side slopes from aerial LiDAR point clouds using open-source Python libraries and qualitatively assess the tool’s performance using rural roadway scenes in western North Carolina. The algorithm isolates the roadway, divides it into short segments, and uses linear regression on adjacent elevation data to determine side slope. We found a local optimum in the side slope goodness-of-fits when using 2.4–3.4 ft road segments and shoulder regions extending 6.4 ft from the roadside. Calculated side slopes generally agreed with elevation maps in Google Earth. We identified areas for improvement of the software, which is being actively developed. While this work is proof-of-concept, the open-source nature of the software and widespread availability of LiDAR data show potential for increasing the incorporation of side slope data in crash severity models, particularly on rural roadways.