Deep-Learning for Lod1 Building Reconstruction from Airborne Lidar Data

Tee Ann Teo*

*Corresponding author for this work

研究成果: Conference contribution同行評審

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/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.

原文English
主出版物標題2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面86-89
頁數4
ISBN(電子)9781538691540
DOIs
出版狀態Published - 七月 2019
事件39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
持續時間: 28 七月 20192 八月 2019

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
國家Japan
城市Yokohama
期間28/07/192/08/19

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