An automatic, rapid and continuous impervious surface mapping framework based on historical land cover datasets

Long time series impervious surface mapping (ISM) is important for understanding urban expansion, environmental impacts, and urban planning. There are some historical global ISM products, such as GAIA and NUACI datasets, whereas they may not meet the user’s diverse application needs in the aspects of mapping timeliness, temporal resolution, and spatial resolution. Therefore, this study proposes an automatic, rapid, and continuous impervious surface mapping and updating framework based on historical land cover datasets without using other labeled data, to improve the updating speed and spatio-temporal resolution of impervious surface maps. The main process is divided into three steps: (1) Multi-temporal samples for classification were obtained by using GAIA dataset, FROM-GLC dataset and the unsupervised continuous change detection (CCD) algorithm; (2) Quarterly long time series ISM results (ISMs) were obtained by using multi-temporal samples and quarterly features; (3) The final results were obtained by using the post-processing operations in the obtained quarterly long time series ISMs to improve the mapping accuracy. The proposed framework is applied to eight cities around the world, and the total overall accuracy (OA) and Kappa of long time series ISMs with post-processing in the eight cities are 92.64% and 0.8525, respectively, improving the OA and Kappa of those without post-processing by 1.41% and 0.0281, respectively, and those of GAIA dataset by 4.57% and 0.0914, respectively, which proved the effectiveness of the proposed method. This study also analyzed the spatial patterns of impervious surface expansion in eight cities and identified different spatial patterns of expansion that existed among the cities, while capturing the abrupt change in the spatial patterns of expansion in Rosario and Novosibirsk after the second quarter of 2021. The proposed framework achieved rapid mapping and updating of impervious surface without any labeled samples, and has the potential to map the global impervious surface continuously.