Developing a two-level machine-learning approach for classifying urban form for an East Asian mega-city

Abstract

Having had the most rapid urbanization in the world since the 1990s, mega-cities in East Asia featured highly compact and atomized modernist architecture. With densely built modernist architecture and relatively free building regulations, it is challenging to trace the actual development of the whole city. Compared to European cities, their overall urban landscapes are much denser, higher, and functionally mixed. In order to achieve a quicker and more accurate identification of urban forms in mega-cities, this study proposed a two-level machine-learning approach. At the building level, we extracted features from topographic maps and building licenses to automatically classify building types. Four state-of-the-art multi-class classification models were compared. At the block level, we used building types as input data and compared two methods for block clustering. In total 61,426 buildings from Taipei were classified and grouped into 10 block types. Different from Western cities, many of the block types in Taipei were mixtures of different types of buildings. This approach is efficient in exploring new urban form types, especially for emerging mega-cities where block types are previously unknown. The result not only sheds light on the features of East Asian urban landscapes but also serves as important basis of type-based strategic plans for contemporary urban issues.