A computer-implemented method of simultaneously operating self-supervised depth and ego-motion prediction neural networks processing a video stream of monocular images, wherein a. the depth neural network learns to output dense depth or disparity for each pixel coordinate of a single image and b. the ego-motion neural network learns to output relative translation and rotation forming an affine transformation between a pair of overlapping images, c. wherein the inputs for the neural networks are a sequence of temporally consecutive image triplets, and d. the output dense depth or disparity of the depth neural network and the relative transformation derived from the ego-motion neural network are linked together via a perspective projection model that performs view synthesis to warp the source images to the target image, such that the center image of the image triplets is target and the model is arranged to synthesize a target image from the first and last source images of the image triplets, e. whereafter the original center target image and the synthesized target image are compared to train the depth and ego-motion prediction neural networks using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions, characterized in that f. complementary Global Positioning System coordinates synchronized with the images are used to calculate a "GPS-to-scale loss" that utilizes a ratio of a relative translation measured by the GPS and a relative translation predicted by the ego-motion network, and g. the final loss function is calculated comprising the appearance based photometric loss and smoothness loss, plus the GPS-to-scale loss function times a relative weight to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation. a. the depth neural network learns to output dense depth or disparity for each pixel coordinate of a single image and b. the ego-motion neural network learns to output relative translation and rotation forming an affine transformation between a pair of overlapping images, c. wherein the inputs for the neural networks are a sequence of temporally consecutive image triplets, and d. the output dense depth or disparity of the depth neural network and the relative transformation derived from the ego-motion neural network are linked together via a perspective projection model that performs view synthesis to warp the source images to the target image, such that the center image of the image triplets is target and the model is arranged to synthesize a target image from the first and last source images of the image triplets, e. whereafter the original center target image and the synthesized target image are compared to train the depth and ego-motion prediction neural networks using an appearance-based photometric loss between real and synthesized target images, as well as a smoothness loss on the depth predictions, characterized in that f. complementary Global Positioning System coordinates synchronized with the images are used to calculate a "GPS-to-scale loss" that utilizes a ratio of a relative translation measured by the GPS and a relative translation predicted by the ego-motion network, and g. the final loss function is calculated comprising the appearance based photometric loss and smoothness loss, plus the GPS-to-scale loss function times a relative weight to enforce the scale-consistency and/or -awareness on the monocular self-supervised ego-motion and depth estimation.