Prompting Gemini with damage-level definitions yields synthetic disaster imagery that a pre-trained classifier labels at F1 around 0.64, close to its score on real images, but the comparison lacks statistical support.
Deep Learning Approach for Data and Computing Efficient Disaster Mitigation in Humanitarian Assistance and Disaster Response Applications,
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Effective Damage Data Generation by Fusing Imagery with Human Knowledge Using Vision-Language Models
Prompting Gemini with damage-level definitions yields synthetic disaster imagery that a pre-trained classifier labels at F1 around 0.64, close to its score on real images, but the comparison lacks statistical support.