Method for geolocating discrete objects from a series of unitary geolocated panoramic images taken by a 360° panoramic camera device of a mobile vehicle on the route of said vehicle, which comprises: a. - at least one succession of steps of taking said unitary semispherical or spherical panoramic images by a mobile vehicle carrying a panoramic camera device and carrying a geolocation system for which a periodicity of taking the photographs producing the unitary panoramic images is determined according to a distance travelled by the vehicle or according to a time frequency, the vehicle speed then determining the interval in meters between said photographs, b. - a succession of steps of recognising and classifying objects, in said unitary panoramic images, performed by means of neural network-based deep learning models producing detection and classification of objects in the panoramic images and producing definition of selection frames around subsets of pixels that contain the objects detected in the panoramic images and, for each object in each panoramic image, a classification code and the coordinates i vertical, j horizontal in number of pixels of a position location of the width selection frame w and height selection h frame containing said object in said panoramic image; c. - a succession of steps (305 to 380) of geolocating the classified objects by a method of triangulating said objects from at least two of said separate unitary panoramic images containing said classified objects and the positions of the panoramic cameras when taking said panoramic images containing said classified objects, said geolocation steps including, for a series of unitary panoramic images and for each detected object: i.- a step of determining, in each unitary panoramic image of said series in which an object classification is present, the angular position with respect to the photographing centre of said detected object, ii.- at least one change of reference of the coordinates of said object in the images in terrestrial Cartesian coordinates according to the coordinates of the cameras at the time of the shootings, iii.- a projection of cones made from an angle α directed towards the centre A of the selection frame of said object, said projection having as apex the centre of the camera device and an opening angle defined by an estimated radius r, per image, equal to half of the smallest of the width w and height h dimensions of the selection frame, iv.- calculating intersections of pairs of cones (110a, 110b) generated in at least two unitary panoramic images including said object and taken at at least two different locations (100a, 100b), so as to calculate by triangulation a spatial distance between detected objects and the positions of the centres of the camera device at said at least two different locations and then to calculate a geolocated position of said detected objects. a. - at least one succession of steps of taking said unitary semispherical or spherical panoramic images by a mobile vehicle carrying a panoramic camera device and carrying a geolocation system for which a periodicity of taking the photographs producing the unitary panoramic images is determined according to a distance travelled by the vehicle or according to a time frequency, the vehicle speed then determining the interval in meters between said photographs, b. - a succession of steps of recognising and classifying objects, in said unitary panoramic images, performed by means of neural network-based deep learning models producing detection and classification of objects in the panoramic images and producing definition of selection frames around subsets of pixels that contain the objects detected in the panoramic images and, for each object in each panoramic image, a classification code and the coordinates i vertical, j horizontal in number of pixels of a position location of the width selection frame w and height selection h frame containing said object in said panoramic image; c. - a succession of steps (305 to 380) of geolocating the classified objects by a method of triangulating said objects from at least two of said separate unitary panoramic images containing said classified objects and the positions of the panoramic cameras when taking said panoramic images containing said classified objects, i.- a step of determining, in each unitary panoramic image of said series in which an object classification is present, the angular position with respect to the photographing centre of said detected object, ii.- at least one change of reference of the coordinates of said object in the images in terrestrial Cartesian coordinates according to the coordinates of the cameras at the time of the shootings, iii.- a projection of cones made from an angle α directed towards the centre A of the selection frame of said object, said projection having as apex the centre of the camera device and an opening angle defined by an estimated radius r, per image, equal to half of the smallest of the width w and height h dimensions of the selection frame, iv.- calculating intersections of pairs of cones (110a, 110b) generated in at least two unitary panoramic images including said object and taken at at least two different locations (100a, 100b),