QCRI/MEDIC

--- license: cc-by-nc-sa-4.0 task_categories: - image-classification language: - en tags: - Disaster - Crisis Informatics pretty_name: 'MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification' size_categories: - 10K<n<100K dataset_info: splits: - name: train num_examples: 49353 - name: dev num_examples: 6157 - name: test num_examples: 15688 --- # MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification ## Data The MEDIC is the largest multi-task learning disaster-related dataset, an extended version of the crisis image benchmark dataset. It consists of data from several sources, including CrisisMMD, data from AIDR, and the Damage Multimodal Dataset (DMD). The dataset contains 71,198 images. ## Table of Contents - [Data format and directories](#data-format-and-directories) - [Disaster response tasks](#disaster-response-tasks) - [Downloads](#downloads) - [Citation](#citation) - [Terms of Use](#terms-of-use) ## Data Format and Directories ### Directories - **data**: Main directory with the following subdirectories: - **aidr_disaster_types/**: Contains images collected using AIDR system for disaster types task. - **aidr_info/**: Contains images collected using AIDR system for informativeness task. - **ASONAM17_Damage_Image_Dataset/**: Damage Assessment Dataset. - **crisismmd/**: CrisisMMD dataset. - **multimodal-deep-learning-disaster-response-mouzannar/**: Damage Multimodal Dataset (DMD). - **MEDIC_train.tsv, MEDIC_dev.tsv, MEDIC_test.tsv**: Training, development, and testing files with specific file formats. - **LICENSE_CC_BY_NC_SA_4.0.txt**: License information. - **terms-of-use.txt**: Terms and conditions. ### Format - **image_id**: Corresponds to the tweet ID from Twitter or ID from the respective source. - **event_name**: Name of the event or data source. - **image_path**: Relative path of the image. - **damage_severity**: Damage severity class label. - **informative**: Informativeness class label. - **humanitarian**: Humanitarian class label. - **disaster_types**: Disaster types class label. ## Disaster Response Tasks 1. **Disaster Types** - Earthquake - Fire - Flood - Hurricane - Landslide - Not disaster - Other disaster 2. **Informativeness** - Informative - Not informative 3. **Humanitarian Categories** - Affected, injured, or dead people - Infrastructure and utility damage - Not humanitarian - Rescue, volunteering, or donation effort 4. **Damage Severity Assessment** - Little or no damage - Mild damage - Severe damage ## Downloads - **MEDIC Dataset, version v1.0**: [Download](https://crisisnlp.qcri.org/data/medic/MEDIC.tar.gz) (11 GB) - **Code**: [GitHub Repository](https://github.com/firojalam/medic) ### License The MEDIC dataset is published under CC BY-NC-SA 4.0 license, which means everyone can use this dataset for non-commercial research purpose: https://creativecommons.org/licenses/by-nc/4.0/. See LICENSE_CC_BY_NC_SA_4.0.txt ## Citation Please cite the following papers if you use this dataset in your research: 1. Firoj Alam, Tanvirul Alam, Md. Arid Hasan, Abul Hasnat, Muhammad Imran, Ferda Ofli. *MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification.* Neural Computing and Applications, 35(3):2609–2632, 2023. [paper](https://link.springer.com/content/pdf/10.1007/s00521-022-07717-0.pdf) [Arxiv](https://arxiv.org/pdf/2108.12828) 2. Firoj Alam, Ferda Ofli, Muhammad Imran, Tanvirul Alam, Umair Qazi. *Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response.* In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020. 3. Firoj Alam, Ferda Ofli, and Muhammad Imran. *CrisisMMD: Multimodal Twitter Datasets from Natural Disasters.* In Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM), 2018, Stanford, California, USA. 4. Hussein Mozannar, Yara Rizk, and Mariette Awad. *Damage Identification in Social Media Posts using Multimodal Deep Learning.* In Proc. of ISCRAM, May 2018, pp. 529–543. 5. Dat Tien Nguyen, Ferda Ofli, Muhammad Imran, and Prasenjit Mitra. *Damage assessment from social media imagery data during disasters.* In Proc. of ASONAM, pages 1–8, Aug 2017. ``` @article{alam2022medic, title={{MEDIC}: A Multi-Task Learning Dataset for Disaster Image Classification}, author={Firoj Alam and Tanvirul Alam and Md. Arid Hasan and Abul Hasnat and Muhammad Imran and Ferda Ofli}, Keywords = {Multi-task Learning, Social media images, Image Classification, Natural disasters, Crisis Informatics, Deep learning, Dataset}, journal={Neural Computing and Applications}, volume={35}, issue={3}, pages={2609--2632}, year={2023}, publisher={Springer} } @InProceedings{crisismmd2018icwsm, author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad}, title = {{CrisisMMD}: Multimodal Twitter Datasets from Natural Disasters}, booktitle = {Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM)}, year = {2018}, month = {June}, date = {23-28}, location = {USA} } @inproceedings{10.1109/ASONAM49781.2020.9381294, author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad and Alam, Tanvirul and Qazi, Umair}, title = {Deep learning benchmarks and datasets for social media image classification for disaster response}, year = {2021}, isbn = {9781728110561}, publisher = {IEEE Press}, url = {https://doi.org/10.1109/ASONAM49781.2020.9381294}, doi = {10.1109/ASONAM49781.2020.9381294}, booktitle = {Proceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining}, pages = {151–158}, numpages = {8}, keywords = {benchmarking, crisis computing, deep learning, disaster image classification, natural disasters, social media}, location = {Virtual Event, Netherlands}, series = {ASONAM '20} } @inproceedings{mouzannar2018damage, title={Damage Identification in Social Media Posts using Multimodal Deep Learning.}, author={Mouzannar, Hussein and Rizk, Yara and Awad, Mariette}, booktitle={ISCRAM}, year={2018}, organization={Rochester, NY, USA} } @inproceedings{nguyen2017damage, title={Damage assessment from social media imagery data during disasters}, author={Nguyen, Dat T and Ofli, Ferda and Imran, Muhammad and Mitra, Prasenjit}, booktitle={Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017}, pages={569--576}, year={2017} } ```许可证：知识共享署名-非商业性使用-相同方式共享4.0（CC BY-NC-SA 4.0） task_categories: - 图像分类（image classification） language: - 英语（en） tags: - 灾难（Disaster） - 危机信息学（Crisis Informatics） pretty_name: 'MEDIC：用于灾害图像分类的多任务学习数据集' size_categories: - 10000 < 样本量 < 100000 dataset_info: splits: - name: 训练集（train） num_examples: 49353 - name: 开发集（dev） num_examples: 6157 - name: 测试集（test） num_examples: 15688 --- # MEDIC：用于灾害图像分类的多任务学习数据集 ## 数据 MEDIC是目前规模最大的多任务学习灾害相关数据集，是危机图像基准数据集的扩展版本。其数据来源于多个数据源，包括CrisisMMD、AIDR系统数据集以及损伤多模态数据集（Damage Multimodal Dataset，DMD）。该数据集共包含71198张图像。 ## 目录 - [数据格式与目录结构](#数据格式与目录结构) - [灾害响应任务](#灾害响应任务) - [数据集下载](#数据集下载) - [引用说明](#引用说明) - [使用条款](#使用条款) ## 数据格式与目录结构 ### 目录结构 - **data/**：主目录，包含以下子目录： - **aidr_disaster_types/**：存储用于灾害类型任务的、通过AIDR系统采集的图像。 - **aidr_info/**：存储用于信息性任务的、通过AIDR系统采集的图像。 - **ASONAM17_Damage_Image_Dataset/**：损伤评估数据集。 - **crisismmd/**：CrisisMMD数据集。 - **multimodal-deep-learning-disaster-response-mouzannar/**：损伤多模态数据集（Damage Multimodal Dataset，DMD）。 - **MEDIC_train.tsv、MEDIC_dev.tsv、MEDIC_test.tsv**：分别对应训练、开发与测试集文件，具备特定格式规范。 - **LICENSE_CC_BY_NC_SA_4.0.txt**：许可证信息文件。 - **terms-of-use.txt**：使用条款文件。 ### 数据格式 各字段说明如下： - **image_id**：对应Twitter推文ID或对应数据源的图像ID。 - **event_name**：事件名称或数据源名称。 - **image_path**：图像相对路径。 - **damage_severity**：损伤程度类别标签。 - **informative**：信息性类别标签。 - **humanitarian**：人道主义相关类别标签。 - **disaster_types**：灾害类型类别标签。 ## 灾害响应任务 1. **灾害类型分类** - 地震 - 火灾 - 洪水 - 飓风 - 山体滑坡 - 非灾害场景 - 其他灾害 2. **信息性分类** - 具有信息价值 - 不具有信息价值 3. **人道主义类别分类** - 受灾、受伤或死亡人员 - 基础设施与公共设施损坏 - 非人道主义相关 - 救援、志愿或捐赠行动 4. **损伤程度评估** - 轻微或无损伤 - 中度损伤 - 严重损伤 ## 数据集下载 - **MEDIC数据集 v1.0版本**：[下载](https://crisisnlp.qcri.org/data/medic/MEDIC.tar.gz)（大小为11 GB） - **代码**：[GitHub仓库](https://github.com/firojalam/medic) ### 许可证 MEDIC数据集采用CC BY-NC-SA 4.0许可证发布，允许所有人将其用于非商业性研究用途：https://creativecommons.org/licenses/by-nc/4.0/。详细条款请参阅`LICENSE_CC_BY_NC_SA_4.0.txt`文件。 ## 引用说明 若您在研究中使用该数据集，请引用以下文献： 1. Firoj Alam, Tanvirul Alam, Md. Arid Hasan, Abul Hasnat, Muhammad Imran, Ferda Ofli. *MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification*. 《Neural Computing and Applications》, 35(3):2609–2632, 2023. [论文原文](https://link.springer.com/content/pdf/10.1007/s00521-022-07717-0.pdf) [ArXiv预印本](https://arxiv.org/pdf/2108.12828) 2. Firoj Alam, Ferda Ofli, Muhammad Imran, Tanvirul Alam, Umair Qazi. *Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response*. 收录于2020 IEEE/ACM国际社会网络分析与挖掘进展会议（ASONAM 2020）, 2020. 3. Firoj Alam, Ferda Ofli, and Muhammad Imran. *CrisisMMD: Multimodal Twitter Datasets from Natural Disasters*. 收录于第12届国际AAAI网络与社交媒体会议（ICWSM 2018）论文集，美国加利福尼亚州斯坦福，2018. 4. Hussein Mozannar, Yara Rizk, and Mariette Awad. *Damage Identification in Social Media Posts using Multimodal Deep Learning*. 收录于ISCRAM 2018会议论文集，2018年5月，第529–543页. 5. Dat Tien Nguyen, Ferda Ofli, Muhammad Imran, and Prasenjit Mitra. *Damage assessment from social media imagery data during disasters*. 收录于2017 IEEE/ACM国际社会网络分析与挖掘进展会议论文集，2017年8月，第1–8页. @article{alam2022medic, title={{MEDIC}: A Multi-Task Learning Dataset for Disaster Image Classification}, author={Firoj Alam and Tanvirul Alam and Md. Arid Hasan and Abul Hasnat and Muhammad Imran and Ferda Ofli}, Keywords = {Multi-task Learning, Social media images, Image Classification, Natural disasters, Crisis Informatics, Deep learning, Dataset}, journal={Neural Computing and Applications}, volume={35}, issue={3}, pages={2609--2632}, year={2023}, publisher={Springer} } @InProceedings{crisismmd2018icwsm, author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad}, title = {{CrisisMMD}: Multimodal Twitter Datasets from Natural Disasters}, booktitle = {Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM)}, year = {2018}, month = {June}, date = {23-28}, location = {USA} } @inproceedings{10.1109/ASONAM49781.2020.9381294, author = {Alam, Firoj and Ofli, Ferda and Imran, Muhammad and Alam, Tanvirul and Qazi, Umair}, title = {Deep learning benchmarks and datasets for social media image classification for disaster response}, year = {2021}, isbn = {9781728110561}, publisher = {IEEE Press}, url = {https://doi.org/10.1109/ASONAM49781.2020.9381294}, doi = {10.1109/ASONAM49781.2020.9381294}, booktitle = {Proceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining}, pages = {151–158}, numpages = {8}, keywords = {benchmarking, crisis computing, deep learning, disaster image classification, natural disasters, social media}, location = {Virtual Event, Netherlands}, series = {ASONAM '20} } @inproceedings{mouzannar2018damage, title={Damage Identification in Social Media Posts using Multimodal Deep Learning.}, author={Mouzannar, Hussein and Rizk, Yara and Awad, Mariette}, booktitle={ISCRAM}, year={2018}, organization={Rochester, NY, USA} } @inproceedings{nguyen2017damage, title={Damage assessment from social media imagery data during disasters}, author={Nguyen, Dat T and Ofli, Ferda and Imran, Muhammad and Mitra, Prasenjit}, booktitle={Proceedings of the 2017 IEEE/ACM international conference on advances in social networks analysis and mining 2017}, pages={569--576}, year={2017} }