WetFramework: A deep learning framework for coastal wetland boundary extraction and inundation frequency estimation

Abstract

Coastal wetlands, characterized by their geomorphological sensitivity and tidal dependence, exhibit pronounced vulnerability under global warming. While the persistent threat of sea-level rise to coastal wetlands has been extensively documented at the macroscale, there remains a lack of systematic quantitative frameworks for mapping these trends to the microscale dynamics of wetland evolution. To address this gap, this paper proposes WetFramework, a novel approach for joint modeling of spatial structure and temporal variation in wetlands. (1) In the encoder, Transformer and Mamba modules are integrated to enhance multiscale feature representation through the synergy of global attention and implicit sequence modeling, with a Token-Driven Attention Mechanism (TDAM) designed to facilitate deep interactions between features. (2) In the decoder, a Wavelet-Enhanced Reconstruction Module (WERM) is introduced to improve spatial structure modeling via wavelet transforms, thereby optimizing boundary delineation and fine detail representation for precise mapping of coastal wetland extents. (3) To capture periodic inundation characteristics, a Fourier-Based Inundation Estimation Module (FBIEM) is further proposed, incorporating tidal-height observations to enable unsupervised modeling of pixel-level hydrological responses and quantitative expression of inundation rhythms. Extensive experiments conducted in four representative coastal regions—Yancheng and Dongying (China), Mont-Saint-Michel Bay (France), and San Francisco Bay (USA)—demonstrate that the proposed framework outperforms state-of-the-art models across multiple evaluation metrics and exhibits robust cross-regional generalization and dynamic modeling capabilities. This study provides an effective paradigm for intelligent remote sensing-based wetland identification and long-term hydrological modeling, and offers key hydrological information to support inundation-dynamics monitoring and management decision-making.