Accelerating Post-Tornado Disaster Assessment Using Advanced Deep Learning Models (2412.18147v1)

Abstract: Post-disaster assessments of buildings and infrastructure are crucial for both immediate recovery efforts and long-term resilience planning. This research introduces an innovative approach to automating post-disaster assessments through advanced deep learning models. Our proposed system employs state-of-the-art computer vision techniques (YOLOv11 and ResNet50) to rapidly analyze images and videos from disaster sites, extracting critical information about building characteristics, including damage level of structural components and the extent of damage. Our experimental results show promising performance, with ResNet50 achieving 90.28% accuracy and an inference time of 1529ms per image on multiclass damage classification. This study contributes to the field of disaster management by offering a scalable, efficient, and objective tool for post-disaster analysis, potentially capable of transforming how communities and authorities respond to and learn from catastrophic events.

• The paper proposes an automated framework using deep learning models YOLOv11 and ResNet50 for rapid post-tornado building damage assessment.
• ResNet50 achieved 90.28% accuracy in classifying five damage levels with an inference time of 1529ms per image, while YOLOv11 detected buildings at 3ms per frame.
• The framework significantly accelerates damage assessment to improve emergency response efficiency and community resilience, despite challenges like data imbalance and image variability.

An Evaluation of Deep Learning Models for Post-Tornado Disaster Assessment

The paper entitled "Accelerating Post-Tornado Disaster Assessment Using Advanced Deep Learning Models" presents a significant contribution to the field of disaster management through the application of sophisticated deep learning techniques. The authors offer a novel automated framework for post-tornado damage assessment that leverages two primary computer vision architectures: YOLOv11 for object detection and classification, and ResNet50 for multiclass damage classification.

In the context of natural disasters, particularly tornadoes, the rapid and accurate assessment of damage is imperative for effective emergency response and efficient resource allocation. Traditional methods rely on manual evaluation, which is time-consuming, resource-intensive, and prone to subjective bias. The proposed method in this research addresses these limitations by introducing a scalable, quick, and objective solution utilizing advanced machine learning techniques.

Methodology and Model Performance

The paper utilizes datasets from two significant tornado events: the 2021 Midwest Tornado and the 2013 Moore Tornado. The data involved images and videos that depict varying degrees of building damage. For data preprocessing, the team implemented techniques tailored to standardize image inputs and enhance model performance through data augmentation.

The ResNet50 model achieved an impressive accuracy of 90.28% in classifying building damage into categories such as undamaged, slight, moderate, extensive, and complete. This classification was accomplished with an inference time of 1529 milliseconds per image. Meanwhile, the YOLOv11 model performed rapid detection of buildings within video frames, achieving an accuracy of 60.83% and operating at 3 milliseconds per frame. The high accuracy of ResNet50 in damage classification demonstrates its effectiveness in distinguishing between varying damage levels, a task which is crucial for prioritizing response efforts.

Implications and Potential Future Work

The implications of this research are profound. By reducing the time needed for damage assessment from weeks down to potentially real-time processing, emergency response teams can greatly enhance their operational efficiency. The framework allows for rapid prioritization of disaster response actions, thus improving the overall resilience of affected communities.

Challenges do remain, such as class imbalance within the datasets and difficulties related to image variability such as occlusions and variations in lighting. The authors suggest further research to expand the geographic scope of testing and to refine preprocessing methods in order to improve model robustness and accuracy further.

In conclusion, this paper underscores the potential of deep learning applications in transforming disaster management and response strategies. As AI continues to mature, similar frameworks could be adapted for other types of natural disasters, thereby broadening the impact of AI in social resilience and disaster recovery operations.