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Post Disaster Damage Assessment Using Ultra-High-Resolution Aerial Imagery with Semi-Supervised Transformers.

Deepank Kumar Singh¹, Vedhus Hoskere¹

¹Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77204, USA.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This summary is machine-generated.

This study introduces an automated preliminary damage assessment (PDA) framework using ultra-high-resolution aerial images and transformer models. The new method surpasses current techniques in accuracy and efficiency for disaster recovery.

Keywords:

aerial imagery preliminary damage assessments semi-supervised learning transformers

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Area of Science:

Disaster Management
Artificial Intelligence
Remote Sensing

Background:

Preliminary damage assessments (PDA) are crucial for disaster recovery.
Traditional door-to-door inspections are slow and inefficient.
Existing automated PDA methods using satellite imagery and CNNs lack sufficient accuracy.

Purpose of the Study:

To develop a more accurate and efficient automated PDA framework.
To improve damage level predictions for entire buildings using novel data and models.

Main Methods:

Utilized ultra-high-resolution aerial (UHRA) images.
Employed state-of-the-art transformer models.
Implemented semi-supervised learning with large unlabeled datasets.

Main Results:

Semi-supervised transformer models achieved superior accuracy and generalization.
The proposed framework outperformed existing PDA methods.
UHRA images combined with transformers overcame limitations of satellite imagery and CNNs.

Conclusions:

The novel PDA framework offers more accurate and efficient building damage assessments.
Semi-supervised learning with UHRA images is key to improving disaster recovery.
This approach enhances governmental resource allocation post-disaster.