Evaluation of Road Segmentation Techniques on Visible and Infrared Low-Altitude UAS Imagery

Road network understanding is an important component of determining where a vehicle can safely maneuver in an area of interest, especially in compromised environment scenarios such as disaster response. Low-altitude unmanned aircraft systems and semantic segmentation via deep neural networks provide an efficient solution to denoting the locations of roads in potentially large swaths of imagery. Unmanned aerial vehicles such as drones can be flown over an area of interest to quickly capture high quality overhead images in multiple modalities such as visible and infrared; and can provide updated imagery as needed, which is important for rapidly changing situations. Meanwhile, deep neural networks can automatically segment the roads in the incoming imagery. In this work we evaluate two semantic segmentation deep neural network architectures, U-Net and DeepLabV3+, and their ability to segment roads in low-altitude UAS imagery in both the visible and infrared modalities. We find that segmenting roads in the infrared imagery is more difficult for the models, and our results indicate that DeepLabV3+ may have the ability to better generalize to unseen data than U-Net when data augmentation is introduced. Our evaluations show an mIoU of 87.33 and 82.47 on the test sets for the visible and infrared domains, respectively, using DeepLabV3+.