Large-scale tidal wetland mapping based on point-sample-driven debiased and label noise learning: A case study in Asia

• A point-sample-driven large-scale tidal wetland mapping framework is proposed. • A covariate-consistent debiased sampling strategy is designed for biased data. • The framework can generate debiased pixel-wise samples with limited points. • A class-conditional noise learning module is proposed for noisy generated labels. • The framework was successfully applied to tidal wetland mapping in Asia. Reliable tidal wetland maps are crucial for coastal monitoring. However, the inherent spatiotemporal variability and dynamic flooding patterns of tidal wetlands pose substantial challenges to conventional remote sensing classification approaches. While deep learning approaches show promise for accurate mapping when sufficient training data exist, large-scale applications face sample scarcity issues. In this paper, given the challenges posed by the complexity of tidal wetland formation and succession on sample quantity and quality, we propose a point-sample-driven tidal wetland mapping method based on debiased sampling and noise-resistant learning. Historical point samples are first converted into pixel-wise pseudo-labels to train a deep learning classifier. To alleviate sample selection bias, covariate statistical features are explicitly modeled, ensuring that the generated pseudo-labels better reflect the true spatial distribution of tidal wetlands. To mitigate semantic errors in pseudo-labels, confusion probabilities between tidal wetland categories are incorporated as prior knowledge, enabling the identification and suppression of label noise. The effectiveness of the proposed method was tested across the tidal regions of Asia, covering a coastline of approximately 3,800,000 km 2 and achieving an extraction accuracy of 85 %. Comparative experiments demonstrate that our framework consistently outperforms existing approaches, yielding substantial improvements in classification accuracy. The mapping results are publicly available at: https://github.com/yingxinwu/AsiaTidalWetland.git