  Geographic information systems (GIS) now provide accurate maps of terrain,
roads, waterways, and building footprints and heights. Aircraft, particularly
small unmanned aircraft systems, can exploit additional information such as
building roof structure to improve navigation accuracy and safety particularly
in urban regions. This paper proposes a method to automatically label building
roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed
to convolutional neural networks (CNN) to extract salient feature vectors.
Supervised training sets are automatically generated from pre-labeled buildings
contained in the OpenStreetMap database. Multiple CNN architectures are trained
and tested, with the best performing networks providing a condensed feature set
for support vector machine and decision tree classifiers. Satellite and LIDAR
data fusion is shown to provide greater classification accuracy than through
use of either data type individually.
