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

Description

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

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