SYSTEM AND METHOD FOR LOCATING THE POSITION OF A ROAD OBJECT BY UNSUPERVISED MACHINE LEARNING

The present disclosure concerns systems, methods and a set of computer programs for precisely locating the position of at least one road object associated with a portion of a road network. The general principle of the disclosure is based on determining the position of a road object using unsupervised classification based on distribution density. In the disclosure, the classification is applied in two phases. In the first phase, it is applied a first time to the plurality of geographic coordinates associated with a road object so as to automatically group it into homogeneous classes. In the second phase, it is applied a second time to each class produced in the first phase, so as to automatically group it into homogeneous subclasses based on the azimuth angles of the road object that are associated with the geographic coordinates of the class.