AutoLand

To faciliate training of neural networks and evaluation of alternate approaches for landing, we provide a synthetic dataset comprised of collapsed buildings. The dataset consists of 1,281,125 RGB images with corresponding groundtruth for depth, surface normals, semantics and camera pose information. In order to have diverse viewing angles, we varied the tilt of the camera from 0◦ to 55◦ in steps of 5◦, the pan of the camera from 0◦ to 360◦ in steps of 45◦, and we also varied the height of the UAV during data collection from 10 m to 30 m in steps of 5 m. Annotations are provided for the following classes: sky, houses, road, rocks, flora, terrain, trees, cars, and others.