Abstract: A system and method for providing consolidated events in a traffic management control centre may include: obtaining a plurality of raw events, each from an event source and each including at least one of a geographical location, timestamp and event type; validating each of the raw events against a set of rules; terminating the raw events for which validation is not successful and leaving validated events; obtaining context data from a plurality of georeferenced dynamic map layers; matching each of the validated events with the georeferenced dynamic map layers using the at least one of geographical location, timestamp and event type, and enriching each of the validated events with the associated context data to generate enriched events; matching each of the enriched events against a plurality of stored events to generate consolidated events; and providing at least one of the consolidated events to a user.

Abstract: Computer-implemented methods and systems for identifying topographic features that optimises and subsequently implements a machine learning model to automatically classify and extract topographic features from a set of target imagery are described herein. In particular, the optimised machine learning model creates heat maps from the target imagery corresponding to each class of feature, wherein the intensity of each pixel indicates whether a certain type of feature is present. The resulting heat maps are then processed to transform each pixel, specifically those identifying a topographic feature, into a geospatial vector.

Abstract: A system and method for determining a viewpoint of a traffic camera includes obtaining images of a real road captured by the traffic camera, segmenting a road surface from the captured images to generate a mask of the real road, generating a 3D model of a simulated road corresponding to the real road, from geographical data of the real road, adding a simulated camera corresponding to the traffic camera to a location in the 3D model that is corresponding to a location of the traffic camera in the real road, generating a plurality of simulated images of the simulated road using the 3D model, each corresponding to a set of viewpoint parameters of the simulated traffic camera, selecting the simulated image that provides the best fit between the simulated image and the mask, and generating mapping between pixel locations in the captured images and locations on the real road.

Abstract: Computer-implemented methods and systems for identifying topographic features that optimises and subsequently implements a machine learning model to automatically classify and extract topographic features from a set of target imagery are described herein. In particular, the optimised machine learning model creates heat maps from the target imagery corresponding to each class of feature, wherein the intensity of each pixel indicates whether a certain type of feature is present. The resulting heat maps are then processed to transform each pixel, specifically those identifying a topographic feature, into a geospatial vector.