用于识别洪水区域的数据集,具有语义细分的语义细分
Naili Suri Intizhami1, Eka Qadri Nuranti1, Nur Inaya Bahar1
1Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia.
Data in brief
|November 29, 2023
概括
这项研究引入了来自Instagram Reels的新洪水图像数据集,用于印度尼西亚Parepare. 该数据集有助于计算机视觉研究识别洪水地区和物体.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 洪水是印度尼西亚经常发生的自然灾害,造成重大破坏和生命损失.
- 社交媒体数据,特别是来自Instagram Reels等平台的数据,越来越多地被用于与灾难有关的信息分析.
- 现有的数据集可能无法完全捕捉洪水事件的动态和多样化的视觉方面.
研究的目的:
- 为了介绍一个新的,大规模的洪水事件数据集,从印度尼西亚帕雷帕雷的Instagram Reels中捕捉到.
- 为计算机视觉研究提供一个有价值的资源,特别是用于洪水受影响地区的语义细分和对象识别.
- 促进用于自动化洪水区识别和损害评估的先进算法的开发.
主要方法:
- 从Instagram Reels收集了7248个视频片段,描绘了帕雷帕雷的洪水事件.
- 将视频数据转换为图像格式,确保各种条件 (区域,时间,视角).
- 预处理图像以提高清晰度,并应用对象注释,用于计算机视觉任务,具有明显的颜色标签.
主要成果:
- 一个包含7248张预处理和注释的洪水事件图像的综合数据集.
- 该数据集从不同角度捕捉到洪水的各种视觉特征.
- 注释有助于计算机视觉模型的培训和评估.
结论:
- 提出的洪水数据集是对用于灾害管理的计算机视觉研究的重要贡献.
- 它使语义细分和对象识别在洪水相关应用中的进步成为可能.
- 该资源支持开发更有效的洪水监测和应对系统.
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