Hurricane damage assessment using coupled convolutional neural networks: a case study of hurricane Michael

Hurricane damage assessment using coupled convolutional neural networks: a case study of hurricane Michael

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Remote sensing provides crucial support for building damage assessment in the wake of hurricanes.This article proposes a coupled deep learning-based model for damage assessment that leverages a large very high-resolution satellite images dataset and a flexibility of building footprint source.Convolutional Neural Networks were used to generate building footprints from pre-hurricane satellite imagery and conduct a classification of incurred damage.We emphasize the advantages of multiclass classification in comparison with traditional binary classification of damage and propose resolving dataset imbalances due to unequal damage impact distribution with a focal loss 7-Piece Sectional with Audio Console function.

We also investigate differences between relying on learned features using a deep learning approach for damage classification versus a commonly used shallow machine learning classifier, Support Vector Machines, that requires manual feature engineering.The proposed model leads to an 86.3% overall accuracy of damage classification Pipe Parts for a case event of Hurricane Michael and an 11% overall accuracy improvement from the Support Vector Machines classifier, suggesting better applicability of such an open-source deep learning-based workflow in disaster management and recovery.Furthermore, the findings can be integrated into emergency response frameworks for automated damage assessment and prioritization of relief efforts.