MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification

Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC\footnote{Available~at: \url{https://crisisnlp.qcri.org/medic/index.html}}, which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on \textit{multi-task learning}, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research.
Other Information
Published in: Zenodo
License: https://creativecommons.org/licenses/by-nc-sa/4.0/
See dataset on publisher's website: https://crisisnlp.qcri.org/medic/

近期灾害信息学的研究表明，人工智能在基于社交媒体内容（文本和图像）的自然灾害救援中具有实际且重要的应用价值，能够拯救人类生命和减轻灾难带来的苦难。尽管在文本处理方面已取得显著进展，但针对图像的利用研究相对较少。为了推进基于图像的方法，我们提出了MEDIC（可在https://crisisnlp.qcri.org/medic/index.html获取），这是迄今为止用于人道主义响应的最大社交媒体图像分类数据集，包含71,198张图像，并在多任务学习框架下解决四个不同的任务。这是首个此类数据集：社交媒体图像、灾害响应和多任务学习研究。该数据集的一个重要特性是其高潜力，能够促进对多任务学习的研究，该领域近期受到机器学习社区的广泛关注，并在记忆、推理速度、性能和泛化能力方面展现出卓越成果。因此，所提出的此数据集对于推进基于图像的灾害管理和多任务机器学习研究具有重要价值。