Road detection is a difficult task because roads are complex objects lacking a cohesive shape; as such, detection is typically limited to satellite images of urban environments. Detecting complex roads beyond asphalt, such as dirt or gravel, in complex environments, such as a desert, poses a more significant challenge. This report describes the implementation of a convolutional neural network to segmentize roads across a variety of types in a variety of environments from aerial images by directly comparing the image to a corresponding ground-truth. Current results are favorable for complex roads but the detector needs more work for environments where roads may take up most of the image.