Current methods for Earth observation tasks such as semantic mapping, map alignment, and
change detection rely on near-nadir images; however, often the first available images in
response to dynamic world events such as natural disasters are oblique. These tasks are
much more difficult for oblique images due to observed object parallax. There has been
recent success in learning to regress an object's geocentric pose, defined as height above
ground and orientation with respect to gravity, by training with airborne lidar registered to …