Extraction and Spatiotemporal Analysis of Impervious Surfaces in Chongqing Based on Enhanced DeepLabv3+

Abstract In this study, Sentinel-2 time series satellite remote sensing imagery and an improved CA-DeepLabV3+ semantic segmentation network were utilized to construct a model for extracting urban impervious surfaces. The model was used to extract the distribution information of impervious surfaces in the central urban area in Chongqing from 2017 to 2022. The spatiotemporal evolution characteristics of the impervious surfaces were analyzed using the area change and standard deviational ellipse methods. The results indicate that the improved CA-DeepLabV3+ model performs exceptionally well in identifying impervious surfaces, with precision, recall, F1 score, and MIoU values of 90.78%, 90.85%, 90.82%, and 83.25%, respectively, which are significantly better than those of other classic semantic segmentation models, demonstrating its high reliability and generalization performance. The analysis shows that the impervious surface area in Chongqing’s central urban area has grown rapidly over the past five years, with a clear expansion trend, especially in the core urban area and its surrounding areas. The standard deviational ellipse analysis revealed that significant directional expansion of the impervious surfaces has occurred, primarily along the north–south axis. This model can achieve large-scale, time-series monitoring of the impervious surface distribution, providing critical technical support for studying urban impervious surface expansion and fine urban management. Future research will further advance the extraction of impervious surfaces based on high-resolution and hyperspectral remote sensing data to obtain more detailed and accurate distribution data, aiding in precise urban management and environmental protection.