🤖 AI Summary
This study addresses the lack of systematically curated remote sensing datasets, which has hindered the application of machine learning across the full disaster management cycle. Through a comprehensive literature review and meta-analysis, this work presents the first unified collection of publicly available remote sensing datasets spanning multiple phases—pre-disaster, during-disaster, and post-disaster—and encompassing diverse hazard types and imaging platforms, including high-resolution satellites and unmanned aerial vehicles (UAVs). The resulting structured and reusable data resource catalog establishes a standardized benchmark for computer vision–driven disaster response research and provides a foundational infrastructure for efficient model development, thereby filling a critical gap in systematic data integration within the field.
📝 Abstract
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.