Datasets for Social Media Image Classification for Disaster Response

Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response During a disaster event, images shared on social media helps crisis managers gain situational awareness and assess incurred damages, among other response tasks. Recent advances in computer vision and deep neural networks have enabled the development of models for real-time image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of damage. Despite several efforts, past works mainly suffer from limited resources (i.e., labeled images) available to train more robust deep learning models. In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment. Moreover, we relabel existing publicly available datasets for new tasks. We identify exact- and near-duplicates to form non-overlapping data splits, and finally consolidate them to create larger datasets. In our extensive experiments, we benchmark several state-of-the-art deep learning models and achieve promising results. We release our datasets and models publicly, aiming to provide proper baselines as well as to spur further research in the crisis informatics community.

用于灾害应急响应的社交媒体图像分类深度学习基准测试与数据集
在灾害发生期间，社交媒体上共享的图像可帮助危机管理人员获取态势感知信息、评估已造成的损失，并完成其他应急响应任务。近年来计算机视觉与深度神经网络领域的研究进展，推动了适用于多项任务的实时图像分类模型的研发，这些任务包括识别灾害事件、筛选无关图像、将图像归类至特定人道主义类别，以及评估灾害损失的严重程度。尽管已有诸多相关研究工作，但过往研究普遍面临训练更鲁棒的深度学习模型所需的标注图像资源匮乏的问题。本研究提出了用于灾害类型检测、信息有效性分类以及损伤严重程度评估的全新数据集。此外，我们对现有的公开可用数据集进行重新标注，以适配新的任务需求；我们通过识别完全重复与近似重复的样本，构建无重叠的数据划分集，并最终将其整合以形成规模更大的数据集。在我们开展的大量对比实验中，我们对多款当前前沿的深度学习模型进行了基准测试，并取得了颇具前景的实验结果。我们将本次研究的数据集与模型公开发布，旨在为危机信息学领域提供规范的基准基线，并推动该领域的进一步研究。

搜集汇总

背景与挑战

背景概述

该数据集专注于灾害响应中的社交媒体图像分类，旨在通过提供标记图像资源来训练更鲁棒的深度学习模型，支持灾害类型检测、信息性分类和损害严重性评估等任务。数据集整合了新的和重新标记的公开数据，通过去重处理确保非重叠分割，并公开了基准模型以促进危机信息学领域的研究。