Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt

TL;DR

This paper introduces JointSeg, a novel framework that combines super-resolution and segmentation to generate high-resolution impervious surface area maps from Sentinel-2 imagery, demonstrating superior accuracy and regional insights in China's Yangtze River Economic Belt.

Contribution

The paper presents a scalable joint framework for super-resolution and segmentation that produces detailed ISA maps directly from freely available satellite data, outperforming existing datasets.

Findings

ISA-1 achieves an F1-score of 85.71%.

Outperforms bilinear interpolation by 9.5%.

Captures urbanization dynamics from 2017 to 2023.

Abstract

We propose a novel joint framework by integrating super-resolution and segmentation, called JointSeg, which enables the generation of 1-meter ISA maps directly from freely available Sentinel-2 imagery. JointSeg was trained on multimodal cross-resolution inputs, offering a scalable and affordable alternative to traditional approaches. This synergistic design enables gradual resolution enhancement from 10m to 1m while preserving fine-grained spatial textures, and ensures high classification fidelity through effective cross-scale feature fusion. This method has been successfully applied to the Yangtze River Economic Belt (YREB), a region characterized by complex urban-rural patterns and diverse topography. As a result, a comprehensive ISA mapping product for 2021, referred to as ISA-1, was generated, covering an area of over 2.2 million square kilometers. Quantitative comparisons against the 10m ESA WorldCover and other benchmark products reveal that ISA-1 achieves an F1-score of 85.71%, outperforming bilinear-interpolation-based segmentation by 9.5%, and surpassing other ISA datasets by 21.43%-61.07%. In densely urbanized areas (e.g., Suzhou, Nanjing), ISA-1 reduces ISA overestimation through improved discrimination of green spaces and water bodies. Conversely, in mountainous regions (e.g., Ganzi, Zhaotong), it identifies significantly more ISA due to its enhanced ability to detect fragmented anthropogenic features such as rural roads and sparse settlements, demonstrating its robustness across diverse landscapes. Moreover, we present biennial ISA maps from 2017 to 2023, capturing spatiotemporal urbanization dynamics across representative cities. The results highlight distinct regional growth patterns: rapid expansion in upstream cities, moderate growth in midstream regions, and saturation in downstream metropolitan areas.