  Modern cameras are equipped with a wide array of sensors that enable
recording the geospatial context of an image. Taking advantage of this, we
explore depth estimation under the assumption that the camera is geocalibrated,
a problem we refer to as geo-enabled depth estimation. Our key insight is that
if capture location is known, the corresponding overhead viewpoint offers a
valuable resource for understanding the scale of the scene. We propose an
end-to-end architecture for depth estimation that uses geospatial context to
infer a synthetic ground-level depth map from a co-located overhead image, then
fuses it inside of an encoder/decoder style segmentation network. To support
evaluation of our methods, we extend a recently released dataset with overhead
imagery and corresponding height maps. Results demonstrate that integrating
geospatial context significantly reduces error compared to baselines, both at
close ranges and when evaluating at much larger distances than existing
benchmarks consider.
