Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
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 assess...
| Authors | Alain P. Ndigande, Josiah Wiggins, Sedat Ozer |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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