Land-cover mapping is important for urban planning and management, and current land-cover mapping products are unable to meet the needs of cities due to frequent land surface changes. In this study, based on the L2HNet network, we generate a high-resolution land-cover mapping product for Wuhan and its surrounding areas. In this paper, we adopt a simplified L2HNet by removing the CAS and the L2H loss module to shorten the cycle time of entire mapping process. The mapping process used ESA LandCover (2021) as LR labels and Google Maps as high-resolution remote sensing images. In the course of the experiment, we also calculate the four indicators MIoU, Oa, FWIoU, and Kappa, evaluate the accuracy of our product in predicting fine feature structure using a point-based test method, and compare it with six mainstream land-cover mapping products. The product achieves a 1m-resolution landcover product in study areas while maintaining an over 75.21% MIoU. Oa, FWIoU and Kappa all maintain values above 85.00%, showing excellent prediction results. In quantitative analysis, compared to ESA LandCover( 2021), the L2HNet product has a significant improvement in mapping accuracy for build-up and permanent water, including an exciting 21.08% improvement in permanent water accuracy and an amazing improvement in build-up. The comparison with mainstream products also shows the credibility and practicality of the product. The end result of this research fills a gap in Wuhan and its surrounding areas' 1m-resolution land-cover mapping product. While significantly improving the product's resolution, L2HNet makes time-and labor-saving periodic mapping a reality.

No abstract