Automatic Framework of Mapping Impervious Surface Growth With Long-Term Landsat Imagery Based on Temporal Deep Learning Model

Ranyu Yin, Guojin He, Guizhou Wang, Tengfei Long, Hongfeng Li, Dengji Zhou, Chengjuan Gong

Abstract: The impervious surface (IS) cover and its dynamics are key parameters in research about urban and ecology. This letter proposed an automatic framework to map the IS growth end-to-end based on the temporal deep learning (DL) model and long time-series Landsat imagery. First, the training and validating datasets were auto-generated by a joint strategy. Then, a DL network was designed, and the IS growth was predicted in temporal windows. Finally, the results from multi-temporal windows are combined to generate the IS growth map. The data around the core of Beijing, China, is tested, and the result shows that the proposed method could: 1) efficiently model the IS growth; 2) map IS growth with less salt-and-pepper noise and false alarm compared to existing products; and 3) be extended to future data easily.