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Smart city article details

Title	Deep-Learning For Lod1 Building Reconstruction From Airborne Lidar Data
ID_Doc	18101
Authors	Teo T.-A.
Year	2019
Published	International Geoscience and Remote Sensing Symposium (IGARSS)
DOI	http://dx.doi.org/10.1109/IGARSS.2019.8897810
Abstract	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/m2. 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. © 2019 IEEE.
Author Keywords	3D building model; CityGML LOD1; deep learning; lidar.

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