  Computational methods to accelerate natural disaster response include change
detection, map alignment, and vision-aided navigation. Current software
functions optimally only on near-nadir images, though off-nadir images are
often the first sources of information following a natural disaster. The use of
off-nadir images for the aforementioned tasks requires the computation of
geocentric pose, which is an aerial vehicle's spatial orientation with respect
to gravity. This study proposes a deep learning ensemble framework to predict
geocentric pose using 5,923 near-nadir and off-nadir RGB satellite images of
cities worldwide. First, a U-Net Fully Convolutional Neural Network predicts
the pixel-wise above-ground elevation mask of the RGB images. Then, the
elevation masks are concatenated with the RGB images to form four-channel
inputs fed into a second convolutional model, which predicts orientation angle
and magnification scale. A performance accuracy of R2=0.917 significantly
outperforms previous methodologies. In addition, outlier removal is performed
through supervised interpolation, and a sensitivity analysis of elevation masks
is conducted to gauge the usefulness of data features, motivating future
avenues of feature engineering. The high-accuracy software built in this study
contributes to mapping and navigation procedures for effective disaster
response to save lives.
