High-resolution (HR) land-cover mapping is an important task for surveying the Earth’s surface and supporting decision-making in sectors such as agriculture, forestry and smart cities. However, it is impeded by the scarcity of HR high-quality labels, complex ground details and high computational cost. To address these challenges, we propose VCNet, a weakly supervised end-to-end deep learning network for large-scale HR land-cover mapping. It leverages easy-access low-resolution (LR) land-cover products as the sole guidance of supervision, fully eliminating the need for manual annotation. In VCNet, we propose a cross-feature learning backbone to learn complete details of various land objects for fine-scale land cover mapping. Besides, it is hybridized with a high-resolution maintaining module and label refining strategies to constantly refine coarse LR labels for guiding the framework training. Extensive experiments in the Chesapeake Bay dataset demonstrate the superiority of VCNet in generating HR land-cover maps from LR labels. Furthermore, we constructed the Tokyo dataset to analyze VCNet’s sensitivity to different LR labels. To verify its practical application potential, VCNet was utilized to produce a 1 m resolution land-cover map for Shanghai (China’s economic epicenter) from a lower resolution (10-m) product, greatly enriching complex ground details. Besides, due to the importance of transportation networks for highly urbanized region, we introduced road category in the practical mapping of Shanghai, which fills a critical gap in traditional land cover classification systems. This contribution offers a scalable solution for evidence-based decision-making in comparable developed regions. Our code is available at: https://github.com/Tusaifei/VCNet.
Tu et al. (Sun,) studied this question.