C2FNet: Cross-Probabilistic Weak Supervision Learning for High-Resolution Land Cover Enhancement

Autonomous large-scale high-resolution land cover (HRLC) mapping remains a major challenge in remote sensing due to the scarcity of reliable training data and resolution mismatches between available labels and input of massive of emerging imagery. Existing global land cover products often suffer from coarse spatial resolution and label noise, limiting their utility for fine-scale urban analysis and environmental monitoring. This article presents C2FNet, a novel Coarse-to-Fine Network designed to generate HRLC maps from noisy, coarse-resolution labels using a weak supervision strategy of the cross-probability. The C2FNet consists of three key modules: 1) edge resolution refinement backbones (ERRBs), which preserve spatial detail via multiscale feature extraction through parallel convolutional branches; 2) unsupervised dynamic shuffle and diagonal annotation (UDSDA), which enhances training reliability by identifying confident regions through spatial-consistency analysis and confidence estimation; and 3) a contrasting self-supervised loss (C2F-Loss) that integrates cross-entropy and cosine similarity terms to mitigate supervision noise and resolution gaps. Evaluations of three benchmark datasets that encompass diverse urban and rural landscapes show that C2FNet achieves state-of-the-art (SoA) performance, with 80.01% overall accuracy (OA) and a Cohen’s kappa score of 0.7567, outperforming SoA models with weak supervision. The dataset and code are available at http://drive.google.com/file/d/1X_Fz7LQIeix3rV3K29FBfKiU1WMdROe-/view