  3D maps are increasingly useful for many applications such as drone
navigation, emergency services, and urban planning. However, creating 3D maps
and keeping them up-to-date using existing technologies, such as laser
scanners, is expensive. This paper proposes and implements a novel approach to
generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free
using Global Navigation Satellite Systems (GNSS) signals, which are globally
available and are blocked only by obstacles between the satellites and the
receivers. This enables us to find the patterns of GNSS signal availability and
create 3D maps. The paper applies algorithms to GNSS signal strength patterns
based on a boot-strapped technique that iteratively trains the signal
classifiers while generating the map. Results of the proposed technique
demonstrate the ability to create 3D maps using automatically processed GNSS
data. The results show that the third dimension, i.e. height of the buildings,
can be estimated with below 5 metre accuracy, which is the benchmark
recommended by the CityGML standard.
