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. <br>

灾害信息学领域的近期研究表明，人工智能可基于社交媒体内容（文本与图像），在自然灾害期间为挽救生命、减轻人类苦痛提供切实且重要的应用场景。尽管基于文本的相关研究已取得显著进展，但针对图像的挖掘研究仍相对不足。为推动基于图像的相关研究进展，我们提出了MEDIC（可访问地址：https://crisisnlp.qcri.org/medic/index.html）——这是目前规模最大的面向人道主义响应的社交媒体图像分类数据集，包含71198张图像，支持多任务学习框架下的四项不同任务。本数据集属于同类首创：首次将社交媒体图像、灾害响应与多任务学习研究三者结合。该数据集的一项重要特性在于，其具备极大潜力推动多任务学习（multi-task learning）领域的研究——近期该方向受到机器学习学界的广泛关注，并在内存占用、推理速度、模型性能与泛化能力等方面展现出优异成果。因此，本数据集可为基于图像的灾害管理研究与多任务机器学习研究提供重要支撑资源。