Building type and built year are critical parameters to infer archetype buildings for urban building en? ergy modeling（UBEM）. Currently，it is difficult to directly obtain these data for most cities. For the building type identification，taking 21 538 building footprints（without a point of interest and community boundary information）in Changsha City as an example，this paper used the random forest algorithm to successfully identify low-rise resi? dences，apartment residences，and other types based on the geometric characteristics，with an overall accuracy of 81.7%. For the determination of built year，7 900 building footprints in the downtown area of Changsha were selected as a case study，and this paper applied the convolutional neural network algorithm to automatically extract building footprints from different historical satellite imageries，with an average precision of 80%. Then，the intersection analy? sis showed that 5 077 buildings were built before 2005，1 606 buildings were built from 2005 to 2014，and 1 217 buildings were built from 2015 to 2017. The proposed method can be easily applied to other cities，and provide data support for UBEM in the future.