Disaster Damage Assessment Using LLMs and Image Analysis

被引：0

作者：

Tani, Masahiro ^{[1
]}

Terao, Makoto ^{[1
]}

Sogi, Naoya ^{[1
]}

Shibata, Takashi ^{[1
]}

Senzaki, Kenta ^{[1
]}

Rodrigues, Royston ^{[2
]}

机构：

[1] Visual Intelligence Research Laboratories, United Kingdom

[2] NEC Laboratories, Singapore

来源：

NEC Technical Journal | 2024年 / 17卷 / 02期

关键词：

Disaster prevention;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Against the backdrop of the intensification of torrential rain disasters in recent years and concerns about the occurrence of a massive earthquake in the near future, there is a growing demand for further strengthening disaster control measures. In the event of a disaster, it is crucial to assess disaster damage quickly and accurately to minimize damage by expediting initial response activities such as evacuation guidance and rescue efforts. In this paper, we introduce a technology that utilizes a multitude of field images collected in the event of a disaster to assess the damage through the use of large language models (LLMs) and image analysis. © 2024 NEC Mediaproducts. All rights reserved.

引用

页码：51 / 53

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