UAVs-based Turkey Earthquake Building Damage Estimation Dataset (UAVs-TEBDE)

The UAVs-TEBDE (Turkey Earthquake Building Damage Estimation) dataset is a high-resolution aerial imagery collection developed to support AI-based post-earthquake damage assessment using deep learning and computer vision. Created in response to the 2023 Turkey earthquakes, the dataset provides annotated building imagery specifically curated for multi-class classification of structural integrity.

The original dataset consists of 1,636 images, each categorized into one of three damage levels:

# Collapsed – Complete structural failure or irreparable destruction (474 images)
#Damaged – Partial failure with visible deformation or cracking (664 images)
#Intact – Structurally stable with no visible signs of damage (498 images)

Imagery was collected using a hybrid acquisition strategy combining:

UAV field missions conducted in the immediate aftermath of the 2023 Turkey earthquakes

Publicly available sources, including:

- Online media platforms (e.g., YouTube, news broadcasts)
-Stock repositories (e.g., Shutterstock, Stock)
-Open datasets (e.g., Kaggle, GitHub)

This multi-source approach ensures a diverse representation of building types, materials, damage patterns, and environmental conditions (e.g., variations in lighting, resolution, and viewing angles), enhancing the dataset’s generalizability for real-world disaster response scenarios.

Data Augmentation Strategy
To address the limited sample size and improve model robustness, a comprehensive image augmentation pipeline was applied to the original dataset. This process generated synthetic but realistic image variants while preserving core structural features.

The augmentation parameters used include:

*Rotation Range: ±160°
*Width Shift: 0.2
*Height Shift: 0.2
*Shear Range: 0.2
*Zoom Range: 0.25
*Horizontal Flip: Enabled
*Fill Mode: Reflect
*Constant Fill Value: 125
*Batch Size: 32
*Augmentation Cycles: 200+

This augmentation strategy increased the total number of samples to 5,500 images per class, resulting in a final dataset size of 16,500 images:

#Collapsed: 5,500 images
#Damaged: 5,500 images
#Intact: 5,500 images

This enhanced version of UAVs-TEBDE offers a balanced, diverse, and high-quality benchmark for training and evaluating advanced building damage detection models.

Code Availability
The related model architecture and training pipeline, including the SCA_HMDA attention module, Vision Transformer, and data augmentation routines, are openly available in the following GitHub repository:
https://github.com/najmulmowla1/Earthquake-Building-Damage-Detection