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Three dimensional calculations of elements of urban form such as buildings and vegetation volumes are necessary for assessment of trends in spatio-temporal sustainable 3D urban form because the volume of vegetation and buildings affect the level of energy consumption and reduction of air pollution. For such studies, the first step is to acquire 3D point clouds of urban form such as by airborne lidar sensors. There is a technical question in respect of pixel and object based building classification approaches on time series lidar data as to which approach is more appropriate for comparing volumetric indicators of 3D urban development sustainability over time. This paper aims to answer this question by comparing the results of building classification of bi-temporal airborne lidar datasets using Support Vector Machines (SVM) algorithm and an object-based classification tool in ERDAS software. The results show that the omission errors for classified building points are not similar for the same buildings in the bi-temporal lidar data sets. This inconsistency affects the measurement of volumetric descriptors of 3D urban form over time. Therefore, two change detection methods of image differencing and SVM are also evaluated. While the level of noise in the SVM change detection results is lower than occurring in results derived by image differencing, the magnitude of change cannot be determined using SVM. Therefore, we conclude that a combination of change detection methods is preferred for spatio-temporal urban analyses.

Building Classification from Lidar Data for Spatio-temporal Assessment of 3D Urban Developments