Since their introduction by Kohonen Self Organizing Maps (SOMs) have been used in various forms for purposes
of surface reconstruction. They offer robust and fast approximations of manifold data from unstructured input
points while being modestly easy to implement. On the other hand SOMs have certain disadvantages when
used in a setup where sparse, reliable and spacial unbounded data occurs. For example, airborne Lidar sensors
generate a continuous stream of point data while flying above terrain. We introduce modifications of the SOM's
data structure to adapt it to unbounded data. Furthermore, we introduce a new variation of the learning rule
called rapid learning that is feasible for sparse but rather reliable data. We demonstrate examples where the
surroundings of an aircraft can be reconstructed in almost real time.