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

UAVs-TEBDE（土耳其地震建筑损毁评估，Turkey Earthquake Building Damage Estimation）数据集是一套高分辨率航空影像集，旨在依托深度学习与计算机视觉技术，支撑基于人工智能的灾后建筑损毁评估工作。本数据集为响应2023年土耳其地震而构建，收录了经标注的建筑影像，专门用于结构完整性的多分类任务。

原始数据集共包含1636张影像，每张影像被划分为三类损毁等级之一：
# 完全坍塌（Collapsed）：结构完全失效或无法修复的损毁（474张）
# 部分损毁（Damaged）：存在可见变形或裂缝的局部失效（664张）
# 完好无损（Intact）：结构稳定且无任何可见损毁迹象（498张）

影像采集采用混合获取方案，整合了两类渠道：
1. 2023年土耳其地震发生后即刻开展的无人机野外作业采集数据
2. 公开可用的外部数据源，具体包括：
- 在线媒体平台（如YouTube、新闻广播）
- 商业素材库（如Shutterstock、Stock）
- 开源数据集平台（如Kaggle、GitHub）

这种多源采集方案确保了建筑类型、建筑材料、损毁模式以及环境条件（如光照差异、分辨率变化、拍摄视角多样性）的多样化呈现，有效提升了数据集在真实灾害响应场景中的泛化能力。

数据增强策略
为缓解原始数据集样本量有限的问题并提升模型鲁棒性，研究团队对原始数据集应用了一套完整的图像增强流程。该流程可生成具备真实感的合成影像变体，同时保留影像中的核心结构特征。

所用增强参数如下：
* 旋转范围：±160°
* 宽度偏移量：0.2
* 高度偏移量：0.2
* 剪切变换范围：0.2
* 缩放范围：0.25
* 水平翻转：启用
* 填充模式：反射填充（Reflect）
* 常量填充值：125
* 批次大小：32
* 增强循环次数：200次以上

该增强策略将每类样本的数量提升至5500张，最终数据集总规模达到16500张影像：
# 完全坍塌：5500张
# 部分损毁：5500张
# 完好无损：5500张

此增强版UAVs-TEBDE数据集为训练与评估先进建筑损毁检测模型提供了均衡、多样且高质量的基准测试集。

代码开源情况
相关模型架构与训练流程（包括SCA_HMDA注意力模块、视觉Transformer（Vision Transformer）以及数据增强代码）已在以下GitHub仓库开源：
https://github.com/najmulmowla1/Earthquake-Building-Damage-Detection