| dc.description.abstract | 3D building model provides spatial information for city planning, construction, and management. Because the reconstruction of building models is still not fully automatic and the cities change rapidly, it would be more preferable to maintain a building database that firstly detect the changes followed by a reconstruction procedure. Therefore, change detection of building model is an important issue for efficiently updating. Traditionally, change detection is usually done using multi-temporal images through the spectral analyses. Those images provide two-dimensional spectral information without including shape in the third dimension. As the availability and quality of emerging LIDAR systems that make the acquisition of shape information convenient, we use new LIDAR point clouds and aerial photos to detect changes for old building model.
The proposed scheme comprises four major parts: (1) data pre-processing, (2) detecting changes on old building areas, (3) finding new or changed buildings, and (4) generation of new building regions. The first step performs the spatial registration for the different types of data. In addition, we remove LIDAR points in ground and vegetation areas. In the second step, we integrate shape and spectral information to determine the change type of building models. We set five change types in this research, namely, unchanged, main-structure changed, micro-structure changed, demolished, and vegetation occluded. In the third step, we search for new or changed buildings by removing non-building points. Finally, we use unchanged building regions and new or changed building points to derive new building regions.
The validation for determination of change type shows that the results can reach 85% overall accuracy. The results for new building regions reach 96% overall accuracy by pixel-based validation. In region-based validation, the commission and omission errors are 4% and 13%, respectively. To provide comprehensive observations, those unreliable results are scrutinized. | en_US |