Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection
This work addresses safety improvements for small unmanned aircraft systems in urban areas by providing incremental enhancements to existing mapping and classification methods.
The paper tackles the problem of automatically classifying building roof shapes to aid multicopter emergency landing site selection, achieving greater classification accuracy by fusing satellite and LIDAR data compared to using either type individually.
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.