Automatic rooftop segment extraction using point clouds generated from aerial high resolution photography.
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Automatically extracting rooftop information from aerial photographs using point cloud generations tools and point cloud plane segmentation algorithms is a interesting and challenging topic. Previous studies on rooftop extraction have used airborne Light Detection And Ranging (LiDAR) derived point clouds or point clouds generated from photographs taken specifically for point cloud generation. We have used photographs taken from the Swedish National Land Survey database to generate point clouds using stereo-matching for rooftop segmentation. Aerial imagery from this data is both cheap and has nationwide coverage. Point cloud generation tools are evaluated based on coverage, point cloud size, geographical precision and point density. We propose a novel combination of property map clipping and rooftop plane segmentation algorithms derived from aerial photography via point cloud generation after comparing promising segmentation algorithms. We conclude that the point clouds generated from the aerial imagery are not sufficient for the implemented method for completely extracting all rooftop segments on a building in an urban environment.
Place, publisher, year, edition, pages
2015. , 38 p.
, UMNAD, 1047
Engineering and Technology
IdentifiersURN: urn:nbn:se:umu:diva-119123OAI: oai:DiVA.org:umu-119123DiVA: diva2:918807
Master of Science Programme in Computing Science and Engineering