標題: DEEP-LEARNING FOR LOD1 BUILDING RECONSTRUCTION FROM AIRBORNE LIDAR DATA
作者: Teo, Tee-Ann
土木工程學系
Department of Civil Engineering
關鍵字: CityGML LOD1;3D building model;deep learning;lidar
公開日期: 1-Jan-2019
摘要: A three-dimensional building model is an important geospatial information for a smart city. The objective of this study is to reconstruct OGC CityGML LOD1 prismatic building models from 3D lidar points automatically. A deep learning approach (i.e. Fully Convolutional Network, FCN) is developed to detect initial building regions for lidar data. After refinement, the building boundary needs regularization to reshape the irregular boundary into a regular building primitive. Finally, a 3D plane fitting is applied to shape the rooftop using lidar points inside building primitive. The test data was an urban area with the size of 1800m by 1200m. The lidar point density was 4 pt/m(2). The experimental result indicated that the proposed method automatically reconstruct the LOD1 block model from lidar data. The accuracy of building detection reached 72% using lidar object height and intensity. The reconstruction showed high similarity with reference LOD1 building model.
URI: http://hdl.handle.net/11536/153988
ISBN: 978-1-5386-9154-0
ISSN: 2153-6996
期刊: 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
起始頁: 86
結束頁: 89
Appears in Collections:Conferences Paper