**Abstract**

Monitoring sustainability of urban form as a 3D phenomenon over time is crucial in the era of smart cities for better planning of the future, and for such a monitoring system, appropriate tools, metrics, methodologies and time series 3D data are required. While accurate time series 3D data are becoming available, a lack of 3D sustainable urban form (3D SUF) metrics, appropriate methodologies and technical problems of processing time series 3D data has resulted in few studies on the assessment of 3D SUF over time. In this chapter, we review volumetric building metrics currently under development and demonstrate the technical problems associated with their validation based on time series airborne lidar data. We propose new metrics for application in spatial and temporal 3D SUF assessment. We also suggest a new approach in processing time series airborne lidar to detect three-dimensional changes of urban form. Using this approach and the developed metrics, we detected a decreased volume of vegetation and new areas prepared for the construction of taller buildings. These 3D changes and the proposed metrics can be used to numerically measure and compare urban areas in terms of trends against or in favor of sustainability goals for caring for the environment.

**Keywords:** point cloud, voxel, time series lidar, 3D spatiotemporal changes, urban remote sensing, machine learning, 3D urban form, support vector machine (SVM) 3D change detection, smart cities
