Advancing coastal land use mapping through deep multi-label classification and multi-sensor data fusion

Coastal environments change and are environmentally sensitive. Land use classification must be accurate and timely for sustainable development, environmental monitoring, and catastrophe risk management. This research introduces a deep learning framework for categorizing coastal land use with multiple labels using high-resolution satellite pictures from several sensors. We design and evaluate a deep convolutional neural network architecture that classifies photos with multiple labels optimally using the MLRSNet dataset, which comprises 60 semantic classes from Chinese coastal locations. Data fusion merges spectral, spatial, and textural characteristics from many remote sensing methods, making classification findings more trustworthy and relevant to more circumstances. Numerous studies have proven that our method accurately separates complex and visually similar coastal categories including wetlands, beaches, rivers, ships, and urban coastlines. Precision, recall, F1-score, and mAP are used to evaluate the model. We also analyze its performance and mistakes in each class. The results demonstrate how deep learning and data fusion may address coastal remote sensing issues such semantic ambiguity, class variability, and class imbalance. This study enhances geographic artificial intelligence (GeoAI) by showing how to create a high-resolution shoreline map using a framework that works from start to end, can be scaled up, and can be utilized elsewhere. The recommended strategy affects environmental monitoring, coastal zone management, and fact-based decision-making, notably with climate change and urbanization along the coastline. Deep learning and multi-sensor satellite technologies can improve operational coastal monitoring systems, according to our findings.