  High resolution data models like grid terrain models made from LiDAR data are
a prerequisite for modern day Geographic Information Systems applications.
Besides providing the foundation for the very accurate digital terrain models,
LiDAR data is also extensively used to classify which parts of the considered
surface comprise relevant elements like water, buildings and vegetation. In
this paper we consider the problem of classifying which areas of a given
surface are fortified by for instance, roads, sidewalks, parking spaces, paved
driveways and terraces. We consider using LiDAR data and orthophotos, combined
and alone, to show how well the modern machine learning algorithms Gradient
Boosted Trees and Convolutional Neural Networks are able to detect fortified
areas on large real world data. The LiDAR data features, in particular the
intensity feature that measures the signal strength of the return, that we
consider in this project are heavily dependent on the actual LiDAR sensor that
made the measurement. This is highly problematic, in particular for the
generalisation capability of pattern matching algorithms, as this means that
data features for test data may be very different from the data the model is
trained on. We propose an algorithmic solution to this problem by designing a
neural net embedding architecture that transforms data from all the different
sensor systems into a new common representation that works as well as if the
training data and test data originated from the same sensor. The final
algorithm result has an accuracy above 96 percent, and an AUC score above 0.99.
