| dc.description.abstract | Population growth and urban expansion lead urban sustainable development a worldwide issue in recent years. Previous studies have used satellite imagery to explore the land use change and analyzed urban growth by correlating the relationships between land uses and driving factors. Most of them investigate the change of urban development in the planar dimension (building area), while few have been focused on the development of urban structure in the third dimension (building height)—the vertical change of urban. According to the urban land monitoring data, the growth of urban area in Taipei City, the capital city of Taiwan, is becoming slow in the past two decades, and the urban area is considered fully saturated. Taipei City, the capital city of Taiwan, is selected as the study area. This study aims to develop an urban model for assessing the development process of urban development of Taipei City, in both 2D and 3D dimensions. The 3D building models of Taipei City in 1969, 1980, 1991, 2002 and 2007 are used to analyze the change of 2D and 3D urban development. In this study, the machine-learning algorithm—artificial neural networks (ANN) model is applied to assess complex and nonlinear 2D and 3D urban development processes, and the 2D and 3D urban development was quantified by two designed parameters, the build area ratio (BAR) and generalized building capacity (GBC).
Results show that the urban area (2D) represents a higher increasing rate before 1991; however, the building height (3D) represents an inverse trend against the urban area, which has a significant increase after 1991. This study considers that in an intensively developed urban, if the land available for constructing new buildings is becoming limited, the urban development could shift from 2D to 3D—the building height. | en_US |