Visual Intelligence in Crisis Response: An Analytical Review of Image Classification Techniques for Natural Disaster Detection

Natural disasters such as floods, earthquakes, wildfires, and landslides continue to inflict widespread disruption on populations, infrastructure, and ecosystems. The unpredictable onset and rapid progression of these events highlight the need for early detection and accurate assessment to support timely emergency response and recovery. Conventional disaster detection methods, which depend on manual observation, static mapping, and expert-driven evaluation, often exhibit limitations including delayed response, restricted scalability, and poor integration of real time and multi-source data. These challenges constrain effective crisis management, especially during large scale emergencies. Recent developments in artificial intelligence (AI) techniques have significantly advanced the field of disaster assessment through automated processing of satellite imagery, remote sensing data, and aerial surveillance. This study presents a comprehensive analytical review of image classification techniques applied to disaster detection and risk evaluation. Key approaches include supervised learning algorithms, convolutional neural networks, DL-based models, and ensemble learning frameworks. The review examines methodological designs, dataset characteristics, model performance, and practical constraints across various disaster types. Despite notable enhancements in classification accuracy and operational efficiency, critical limitations remain related to real time deployment, dataset imbalance, limited generalization across regions, and lack of multimodal data integration. This work specifically addresses these issues by identifying common shortcomings in existing AI-based disaster detection frameworks and mapping them to potential solution pathways. The analysis further highlights opportunities for developing explainable, resource-efficient, and context-aware models capable of integrating heterogeneous, multimodal, and spatiotemporal data for scalable and timely disaster management. The findings outlined in this review offer valuable insights for the development of intelligent, scalable, and context aware models aimed at strengthening disaster resilience, supporting early warning mechanisms, and enhancing post event recovery planning.