Abstract
The Qinghai–Tibetan plateau is undergoing severe grassland degradation, commonly known as black-soil areas, caused by overgrazing, climate change, and rodent activity. Accurate black-soil area detection is critical for guiding ecological restoration. However, obtaining large-scale annotated datasets is costly due to the ambiguous visual characteristics and high ecological variability of black-soil areas, often necessitating expert validation and repeated refinement. To address this challenge, we propose SBLS (Semi-supervised Black-Soil area detection), a semi-supervised approach that leverages limited labeled data alongside abundant unlabeled imagery. SBLS adopts a cross-branch pseudo supervision strategy, where pseudolabels generated from weakly augmented views in one branch supervise four strongly augmented views in the other branch. To further capitalize on the unlabeled data, we implement a dual-level contrastive learning approach that operates across both low-level and high-level feature spaces of strongly augmented pairs. Experiments demonstrate that SBLS significantly outperforms existing state-of-the-art methods, establishing a new benchmark for black-soil area detection in semi-supervised settings.