2018

Building Damage Annotation on Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks

Cao, Quoc Dung, Choe, Youngjun

Understand

After a hurricane, damage assessment is critical to emergency managers for efficient response and resource allocation.

One way to gauge the damage extent is to quantify the number of flooded/damaged buildings, which is traditionally done by ground survey.
This process can be labor-intensive and time-consuming.
In this paper, we propose to improve the efficiency of building damage assessment by applying image classification algorithms to post-hurricane satellite imagery.

Built on

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