Multisensor Remote Sensing and AI-Driven Analysis for Coastal and Urban Resilience Classification

Abstract

Urban resilience is essential for cities to endure and adjust to environmental and socioeconomic upheavals. The static indicators and rule-based spatial frameworks that are the mainstays of traditional resilience assessment models frequently fall short of capturing the dynamic character of coastal and urban resilience. This article suggests a deep learning-based categorization framework for identifying resilience levels in urban and coastal settings by combining long short-term memory (LSTM) networks with multisensor remote sensing data. The Copernicus Marine Data Service's spatiotemporal ocean physics data, namely the eastward (uo) and northward (vo) seawater velocity, are used in the model to increase the precision of resilience evaluations. The methodology includes a multistep deep learning pipeline, incorporating data preprocessing, feature extraction, class balancing with SMOTE, and LSTM-based classification. The proposed LSTM model is optimized to enhance performance with dropout regularization (0.3), an Adam optimizer (learning rate = 0.0003), and class weighting strategies. The model is evaluated using accuracy, F1-score, confusion matrices, and loss curves, ensuring reliable classification across different resilience categories. Results indicate that the framework achieves high classification accuracy (91.5%), demonstrating superior performance compared to traditional machine learning approaches. Regarding multisensor fusion and deep learning, this study provides a scalable, adaptive, and data-driven solution for resilience classification, supporting climate adaptation strategies, disaster risk management, and sustainable urban development. The proposed methodology offers a robust tool for policymakers and urban planners, enabling more effective resilience monitoring and decision-making in rapidly evolving urban and coastal environments.