Roof Shape Classification from LiDAR and Satellite Image Data Fusion Using Supervised Learning.

ABSTRACT: Geographic information systems (GIS) provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems (UAS), can exploit this and additional information such as building roof structure to improve navigation accuracy and safely perform contingency landings particularly in urban regions. However, building roof structure is not fully provided in maps. This paper proposes a method to automatically label building roof shape from publicly available GIS data. Satellite imagery and airborne LiDAR data are processed and manually labeled to create a diverse annotated roof image dataset for small to large urban cities. Multiple convolutional neural network (CNN) architectures are trained and tested, with the best pe

SUBMITTER: Castagno J 

PROVIDER: S-EPMC6264004 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature