Leveraging deep learning for coastal monitoring: A VGG16-based approach to spectral and textural classification of coastal areas with sentinel-2A data

Abstract

Coastal ecosystems are vital for the planet's health, providing essential habitats for diverse species and supporting human communities. However, these complex environments face increasing threats from climate change and human activities. Effective monitoring of these areas requires large-scale, efficient, and accurate methods. This study explores the potential of deep learning for automated coastal land cover classification using Sentinel-2A satellite imagery on the Google Earth Engine (GEE) platform. We investigate the impact of transfer learning and spectral band combinations on classification accuracy for five coastal types: artificial, bedrock, sandy, muddy, and vegetation-covered. Our findings demonstrate that a pre-trained VGG16 Convolutional Neural Network (CNN) with transfer learning significantly improves classification accuracy (average 19.3% increase) compared to using default weights. Notably, including the near-infrared (NIR) band in training data leads to superior results, particularly for artificial and bedrock coastlines, where the NIR band's effectiveness in separating land-water boundaries enhances classification accuracy. These results highlight the potential of deep learning for large-scale, automated coastal monitoring, informing applications in sustainable fisheries management, coastal vulnerability assessment, and marine ecosystem conservation.