灾害类型和信息性数据集

本研究中，卡塔尔计算研究机构开发了新的灾害类型和信息性数据集，用于支持灾害响应中的图像分类任务。数据集包含约30,000张图像，这些图像来自多个灾害事件，包括洪水、地震、火灾等，并经过严格的手动标注。数据集的创建过程涉及从社交媒体和搜索引擎中收集图像，移除重复和无关图像，并确保数据集的多样性和覆盖性。这些数据集主要用于训练深度学习模型，以自动分类和识别灾害相关图像，帮助危机管理者快速获取情境意识和评估损害程度。数据集的应用领域包括灾害类型检测、信息性分类、损害严重性评估等，旨在提高灾害响应的效率和准确性。

In this study, the Qatar Computing Research Institute developed a novel dataset for disaster type classification and informative image processing to support image classification tasks in disaster response. The dataset contains approximately 30,000 images sourced from multiple disaster events including floods, earthquakes, wildfires, and others, which have undergone rigorous manual annotation. The dataset creation process involves collecting images from social media platforms and search engines, removing duplicate and irrelevant images, and ensuring the dataset's diversity and coverage. This dataset is primarily used to train deep learning models for automatically classifying and identifying disaster-related images, helping crisis managers rapidly acquire situational awareness and assess damage severity. Its application scenarios include disaster type detection, informative image classification, damage severity assessment, and more, aiming to enhance the efficiency and accuracy of disaster response operations.