As an alternative to expensive road surveys, we are working toward a method to infer the road network from GPS data logged from regular vehicles. One of the most important components of this problem is to find road intersections. We introduce an intersection detector that uses a localized shape descriptor to represent the distribution of GPS traces around a point. A classifier is trained on the shape descriptor to discriminate intersections from non-intersections, and we demonstrate its effectiveness with an ROC curve. In a second step, we use the GPS data to prune the detected intersections and connect them with geometrically accurate road segments. In the final step, we use the iterative closest point algorithm to more accurately localize the position of each intersection. We train and test our method on GPS data gathered from regular vehicles in the Seattle, WA, USA area. The tests show we can correctly find road intersections.