DanielCruz09/natural_disasters

Natural Disaster Image Dataset

Summary

This dataset is based on AIDERv2 (Aerial Image Dataset for Emergency Response Applications), published by Shianios and Kyrkou (2023). The dataset contains 16,723 images divided into four classes:

• Earthquakes
• Fires/Wildfires
• Floods
• Normal (no damage)

This dataset contains two files:

• raw: The raw images files.
• processed: Preprocessed (resized and normalized) image files.

These images can be used to train a Convolutional Neural Network for natural disaster image classification.

Dataset Access

Due to size limitations, the dataset is hosted externally in Google Drive. Download the dataset here.

The dataset is structured in the following format:

├── data
│   ├── raw
│   │   ├── Train
│   │   │   ├── Earthquake
│   │   │   ├── Fire
│   │   │   ├── Flood
│   │   │   ├── Normal   
│   │   ├── Test
│   │   │   ├── Earthquake
│   │   │   ├── Fire
│   │   │   ├── Flood
│   │   │   ├── Normal 
│   │   ├── Val
│   │   │   ├── Earthquake
│   │   │   ├── Fire
│   │   │   ├── Flood
│   │   │   ├── Normal 
│   ├── processed
│   │   ├── Train
│   │   │   ├── Land_Disaster
│   │   │   ├── Fire_Disaster
│   │   │   ├── Water_Disaster
│   │   │   ├── Non_Damage 
│   │   ├── Test
│   │   │   ├── Land_Disaster
│   │   │   ├── Fire_Disaster
│   │   │   ├── Water_Disaster
│   │   │   ├── Non_Damage 
│   │   ├── Val
│   │   │   ├── Land_Disaster
│   │   │   ├── Fire_Disaster
│   │   │   ├── Water_Disaster
│   │   │   ├── Non_Damage 

The original dataset can be found at Zenodo.

References

[1] Shianios, D., Kyrkou, C., Kolios, P.S. (2023). A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_24. Link: https://link.springer.com/chapter/10.1007/978-3-031-44240-7_2

[2] D. Shianios, P. Kolios, C. Kyrkou, "DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition", SN Computer Science, 2024 (Accepted to Appear)