Online Damage

Online damage assessment focuses on rapidly and accurately characterizing damage to structures and infrastructure using remote sensing and machine learning. Current research emphasizes the development and application of deep learning models, including convolutional neural networks (CNNs), transformers, and object detection architectures like YOLO, often incorporating multi-modal data (e.g., satellite imagery, street view images, sensor data) and leveraging techniques like domain adaptation and active learning to improve prediction accuracy and efficiency. This field is crucial for disaster response, infrastructure management, and structural health monitoring, enabling faster and more informed decision-making in the face of damage events.