Starting from raw GNSS position with a lot of noise and scattered patterns machine learning algorithm such as random forests help to improve the classification of GNSS positions into "good" and "bad" ones.
Construction site vehicles are sending regularly their position to a server and a central unit is interested in observing the locations or tracking the objects and see where they had been in the past. As a matter of fact, these positions are very often scattered especially when the vehicles are hardly moving or at a stand still. Nevertheless they receive signals and send their positions to the central unit. This leads to a fair amount of data that can be significantly reduced. This presentation shows how with machine learning approaches GNSS positions can be classified as good or bad ones and how the bad ones that are usually scattered and for clusters can be grouped and reduced to a centroid. As a result the amount of data is massively reduced and is a lot more manageable for usage in other applications that are sensitive to data volume or have a minor bandwidth to access and display the data in a map context.
Speakers: Hans-Jörg Stark