梳理遥感灾害管理数据集，助力快速响应灾情
Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
- 系统整理灾害全周期遥感图像数据集
- 覆盖灾前、灾中、灾后各阶段应用需求
- 为研究者提供高质量数据参考
近年来的自然灾害凸显了高效数据驱动方法在灾害管理中的紧迫需求。机器学习（ML）和深度学习（DL）技术在减灾、准备、检测、响应和恢复等关键环节展现出巨大潜力。然而，基于遥感的ML/DL应用成功的关键在于标注数据集的可获取性与质量。随着无人机（UAV）和卫星高分辨率影像的日益普及，计算机视觉与遥感算法已成为灾情快速检测、态势评估与决策支持的重要工具。本综述全面梳理了公开可用的图像数据集，涵盖支持机器学习/深度学习灾害管理全流程的计算机视觉与遥感任务。重点关注灾前、灾中及灾后各阶段的数据资源。本文旨在为研究人员和从业者提供一个集中化的数据集参考，加速遥感驱动的灾害应急解决方案的研发与部署。
原文摘要 · Abstract (English)
Recent natural disasters have highlighted the urgent need for efficient data-driven approaches to disaster management. Machine learning (ML) and deep learning (DL) techniques have shown considerable promise in enhancing the key phases of disaster management including mitigation, preparedness, detection, response, and recovery. A critical enabler of successful ML or DL based applications in remote sensing, however, is the accessibility and quality of annotated datasets. With the growing availability of high-resolution imagery from unmanned aerial vehicles (UAVs) and satellites, computer vision and remote sensing algorithms have become essential tools for rapid detection, situational assessment, and decision-making in disaster scenarios. This survey provides a comprehensive overview of publicly available image-based datasets relevant to ML/DL-based disaster management pipelines. Emphasis is placed on datasets that support computer vision and remote sensing tasks across all phases of disaster events including pre-disaster, during, and post-disaster. The goal of this work is to serve as a centralized reference for researchers and practitioners seeking high-quality datasets for rapid development and deployment of remote sensing-driven disaster response solutions.
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