Long Time-Series Analysis Of Urban Development Based On Effective Building Extraction

The effective detection of urban development is the basis of understanding urban sustainability. Although various studies concentrated on long-time-series analysis on urban development, the resolution of images was too low to focus on a single object. In this paper, we provide a long-time-series analysis of built-up areas at an annual frequency in Beijing, China, from 2000 to 2015, based on the automatic building extraction and high-resolution satellite images. We propose a deep-learning based method to extract buildings, and employ an ensemble learning method to improve the localization of boundaries. The time-series results of built-up areas are analyzed based on two schemes, i.e., change detection over the past fifteen years and evaluation of the whole region in three selected years. Our proposed method achieves an average overall accuracy (OA) of 93%. The results reveal that Beijing developed more rapidly during 2001-2008 than other periods in terms of the density and the number of buildings.