Deep Learning Based Damage Detection on Post-Hurricane Satellite Imagery

07/04/2018

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After a hurricane, damage assessment is critical to emergency managers and first responders. To improve the efficiency and accuracy of damage assessment, instead of using windshield survey, we propose to automatically detect damaged buildings using image classification algorithms. The method is applied to the case study of 2017 Hurricane Harvey.

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Deep Learning Based Damage Detection on Post-Hurricane Satellite Imagery

An Attention-Based System for Damage Assessment Using Satellite Imagery

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Fully convolutional Siamese neural networks for buildings damage assessment from satellite images

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Deep Learning Based Damage Detection on Post-Hurricane Satellite Imagery

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An Attention-Based System for Damage Assessment Using Satellite Imagery

Satellite imagery analysis for operational damage assessment in Emergency situations

Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features

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Fully convolutional Siamese neural networks for buildings damage assessment from satellite images

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