DOI: 10.5281/zenodo.10891053
title: AIDERv2 (Aerial Image Dataset for Emergency Response Applications)

description: SUMMARY OF DATASET

 

• This dataset consist of 16,723 aerial images divided into 4 classes.

 

• The dataset contains three commonly occurring natural disasters

earthquake/collapsed buildings, flood, wildfire/fire, and a normal class; do not reflect any disaster

 

• The images can be loaded as numpy arrays using Python programming language and then used to train a Convolutional Neural Network to detect natural disasters from aerial images.

 

• The images are resized to 224x224x3 (heighty,width,channel number) when loaded as numpy arrays.

 

• The dataset is an extension of the AIDER dataset (Aerial Image Dataset for Emergency Response Applications). 

 

• Additional images were collected by open source databases and extracted images as frames of videos downloaded from YouTube. 

 

 

The table below shows the number of images in each set.

 

                              Train     Validation     Test     Total

     Earthquakes     1927     239                239     2405

              Floods     4063     505                502     5070

                   Fire     3509     439                436     4384

            Normal     3900     487                477     4864

                 Total     13399   1670              1654   16723

 

 

If you use this dataset please cite the following publications:

 

[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_24

 

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

 

 

 

 

DATASET FOLDERS FORMAT

 

└───data

│   │

│   └───Dataset_Images

│       │   └───Train

│   │   │    |    └───Earthquake

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Flood

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Normal

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Wildfire

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│       │   └───Val

│   │   │    |    └───Earthquake

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Flood

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Normal

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Wildfire

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│       │   └───Test

│   │   │    |    └───Earthquake

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Flood

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Normal

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

│   │   │    |    └───Wildfire

│   │   │    | img (1).jpg

│   │   │    | img (2).jpg

│   │   │    | .....

 

 

 

 

 

DATA SOURCES AND DATA COLLECTION

 

OPEN SOURCE DATABASES

 

└───AIDER 

https://zenodo.org/record/3888300#.Yuu11nZBxD-

Kyrkou, C. and Theocharides, T., 2020. EmergencyNet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.1687-1699.

 

└───ERA 

https://lcmou.github.io/ERA_Dataset/

Mou, L., Hua, Y., Jin, P. and Zhu, X.X., 2020. Era: A data set and deep learning benchmark for event recognition in aerial videos [software and data sets]. IEEE Geoscience and Remote Sensing Magazine, 8(4), pp.125-133.

@article{eradataset,

        title = {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},

        author = {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},

        journal = {IEEE Geoscience and Remote Sensing Magazine},

        year = {in press}

}

 

 

 

└───ISBDA

https://drive.google.com/file/d/1kEKJ8kr1aScXz_1El7Mn-Yi0ANducQIW/view

Zhu, X., Liang, J. and Hauptmann, A., 2021. Msnet: A multilevel instance segmentation network for natural disaster damage assessment in aerial videos. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2023-2032).

@misc{zhu2020msnet,

    title={MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos},

    author={Xiaoyu Zhu and Junwei Liang and Alexander Hauptmann},

    year={2020},

    eprint={2006.16479},

    archivePrefix={arXiv},

    primaryClass={cs.CV}

}

 

 

└───Floods 2013

https://github.com/cvjena/eu-flood-dataset

Barz, B., Schröter, K., Münch, M., Yang, B., Unger, A., Dransch, D. and Denzler, J., 2019. Enhancing flood impact analysis using interactive retrieval of social media images. arXiv preprint arXiv:1908.03361.

@article{barz2019enhancing,

  title={Enhancing flood impact analysis using interactive retrieval of social media images},

  author={Barz, Bj{\"o}rn and Schr{\"o}ter, Kai and M{\"u}nch, Moritz and Yang, Bin and Unger, Andrea and Dransch, Doris and Denzler, Joachim},

  journal={arXiv preprint arXiv:1908.03361},

  year={2019}

}

 

└───Wildfire Research

http://wildfire.fesb.hr/index.php?option=com_content&view=article&id=58&Itemid=54

 

 

└───PyImages

https://drive.google.com/file/d/1NvTyhUsrFbL91E10EPm38IjoCg6E2c6q/view

The dataset was curated by PyImageSearch reader, Gautam Kumar.

 

 

 

 

YOUTUBE VIDEOS

 

└───Collapsed Buildings/Earthquakes

• https://www.youtube.com/watch?v=TMow3WPcZrQ&t=133s&ab_channel=GORKHALYFOUNDATION

• https://www.youtube.com/watch?v=_HT0tYKKjBI&t=47s&ab_channel=Effect.org

• https://www.youtube.com/watch?v=rkb3y6K3waU

• https://www.youtube.com/watch?v=yir6ArRZY4o&t=109s&ab_channel=UnicefUK

• https://www.youtube.com/watch?v=CM9APmIR9Fk&ab_channel=ToonsZilla

• https://www.youtube.com/watch?v=tmx2w6drAeU&ab_channel=AssociatedPress

• https://www.youtube.com/watch?v=kuSEe8Emwrk&ab_channel=BloombergQuicktake%3ANow

• https://www.youtube.com/watch?v=qoFHA3-m5ag&ab_channel=NBCNews

• https://www.youtube.com/watch?v=MM3PToqEPhQ&ab_channel=GuardianNews

• https://www.youtube.com/watch?v=zB_-TRnGuZE&ab_channel=DISASTERNEWS

• https://www.youtube.com/watch?v=TqAMQQOEsBs&ab_channel=WHAS11

• https://www.youtube.com/watch?v=0ixjTt-jmok&ab_channel=EveningStandard

• https://www.youtube.com/watch?v=bNGA8Ms3d70&ab_channel=CatersClips

• https://www.youtube.com/watch?v=wJ-2d5t23Lg&ab_channel=DailyDose

• https://www.youtube.com/watch?v=ewUcI7I6Gf4&ab_channel=NBCNews

• https://www.youtube.com/watch?v=Wx1cjOdlMZ4&ab_channel=ABCNews%28Australia%29

• https://www.youtube.com/watch?v=jiMK_sVmbXk&t=12s&ab_channel=NewChinaTV  

• https://www.youtube.com/watch?v=M9au_9A2YRo&ab_channel=GuardianNews

• https://www.youtube.com/watch?v=i6Lh8IXPjso&ab_channel=TheSun

• https://www.youtube.com/watch?v=CKwxEr3I4Y8&ab_channel=GuardianNews

• https://www.youtube.com/watch?v=hxqzcajBCNg&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=3&ab_channel=WXChasing

• https://www.youtube.com/watch?v=2GEeTDuf9mI&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=6&ab_channel=WXChasing

• https://www.youtube.com/watch?v=bDOuZWxIyNQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=9&ab_channel=WXChasing

• https://www.youtube.com/watch?v=vzoSADijLCQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=15&ab_channel=WXChasing

• https://www.youtube.com/watch?v=ZaL1fldTEAk&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=17&ab_channel=WXChasing

• https://www.youtube.com/watch?v=QSV81FdilZE&ab_channel=GlobalNews

• https://www.youtube.com/watch?v=KgOk9otW1Bg&ab_channel=EricFeijten

 

 

 

└───Floods

• https://www.youtube.com/watch?v=DJqgv8Sa5bA&t=317s&ab_channel=7NEWSAustralia

• https://www.youtube.com/watch?v=w5FintiCLJU&t=9s&ab_channel=GuardianNews

• https://www.youtube.com/watch?v=HjMymNN6Ajc&t=143s&ab_channel=BioLogicTreeServices

• https://www.youtube.com/watch?v=Tmba18C94C8&ab_channel=AL.com

• https://www.youtube.com/watch?v=Dqvpv4Vg4lk&t=63s&ab_channel=ElevenEleven

• https://www.youtube.com/watch?v=N7QGicNtN2A&ab_channel=PKSVideoProductions

• https://www.youtube.com/watch?v=8CHagyQG16Q&ab_channel=Stolly-Sven

• https://www.youtube.com/watch?v=vjH3zFqdzcE&ab_channel=BenChilders

• https://www.youtube.com/watch?v=GFw89UB4fE8&ab_channel=BenChilders

• https://www.youtube.com/watch?v=heP3LEJ_NkE&ab_channel=7NEWSAustralia

 

 

 

└───Fires

• https://www.youtube.com/watch?v=gbM_NPx2GPc&t=201s&ab_channel=WXChasing

• https://www.youtube.com/watch?v=M97sJdyeEM4&t=72s&ab_channel=Sanuck176

• https://www.youtube.com/watch?v=1Z2K6lDt76M&t=557s&ab_channel=TheRelaxationChannel

 

└───Normal

• https://www.youtube.com/watch?v=SyxjsuNHWhM&t=328s&ab_channel=OneManWolfPack

• https://www.youtube.com/watch?v=f1PTWsBtrtc&ab_channel=ChernobylPug