Advances in machine learning for wetland classification: a comprehensive survey of methods and applications

Wetlands are critical ecosystems supporting biodiversity and providing essential environmental services. With increasing threats to wetlands, efficient classification techniques are essential for effective conservation. Several researchers have contributed to wetland classification across reputable journals. However, some challenges (data scarcity, noisy labels, and model generalisability) still exist. Therefore, this paper presents a comprehensive survey of recent advancements in machine learning (ML) and deep learning (DL) for wetland classification, focusing on developments from 2018 to 2025. Key methodologies, including convolutional neural networks, transformers, and generative adversarial networks, are critically reviewed, highlighting their strengths, limitations, and applications in remote sensing. Unlike previous reviews, this work emphasises underexplored techniques such as few-shot learning and Mamba networks, offering practical recommendations for handling limited training data and improving model generalisability. The study also identifies promising research directions, such as test-time training and hybrid loss functions, to address challenges in wetland classification. This survey aims to guide researchers and practitioners in advancing state-of-the-art wetland classification through ML and DL technologies.