Combining geolocation and height estimation of objects from street level
  imagery
Matej Ulicny Vladimir A. Krylov Julie Connelly Rozenn Dahyot
We propose a pipeline for combined multi-class object geolocation and height
estimation from street level RGB imagery, which is considered as a single
available input data modality. Our solution is formulated via Markov Random
Field optimization with deterministic output. The proposed technique uses image
metadata along with coordinates of objects detected in the image plane as found
by a custom-trained Convolutional Neural Network. Computing the object height
using our methodology, in addition to object geolocation, has negligible effect
on the overall computational cost. Accuracy is demonstrated experimentally for
water drains and road signs on which we achieve average elevation estimation
error lower than 20cm.