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We propose a pipeline for combined multi-class object geolocation and heightestimation from street level RGB imagery, which is considered as a singleavailable input data modality. Our solution is formulated via Markov RandomField optimization with deterministic output. The proposed technique uses imagemetadata along with coordinates of objects detected in the image plane as foundby a custom-trained Convolutional Neural Network. Computing the object heightusing our methodology, in addition to object geolocation, has negligible effecton the overall computational cost. Accuracy is demonstrated experimentally forwater drains and road signs on which we achieve average elevation estimationerror lower than 20cm.

Combining geolocation and height estimation of objects from street level imagery