从卫星图像测量人类和经济活动,以支持COVID-19大流行期间的城市规模决策
Rodrigo Minetto1, Mauricio Pamplona Segundo2, Gilbert Rotich2
1Universidade Tecnológica Federal do Paraná (UTFPR) Curitiba 80230-901 Brazil.
概括
使用深度学习的卫星图像分析可以自动检测COVID-19大流行引起的经济变化. 这种方法有助于决策者通过视觉数据跟踪全球经济活动.
科学领域:
- 计算机科学 计算机科学
- 遥感 遥感 遥感 遥感
- 经济学 经济学 经济学
背景情况:
- 随着COVID-19的流行,全球经济活动和社会行为发生了全球性的破坏.
- 卫星图像为监测这些变化提供了一个独特的视角.
- 传统的经济指标可能无法捕捉实时的局部影响.
研究的目的:
- 开发和验证深度学习框架,自动检测和分类卫星图像的经济变化.
- 评估框架对经济指标的时间分析能力.
- 为决策者提供一个工具,帮助他们理解和应对疫情引起的经济转变.
主要方法:
- 使用了深度学习方法,结合了战略位置采样和轻量级卷积神经网络 (CNN) 的集合.
- 将框架应用于美国IARPA世界功能地图 (fMoW) 数据集进行时间分析.
- 在COVID-19爆发前后的高分辨率空中场景上展示了结果.
主要成果:
- 在美国国防部的xView挑战中,CNN整体框架实现了高性能,排名第三.
- 在卫星图像中成功识别出表明经济活动的特定元素.
- 经济变化在COVID-19爆发前和后的可测量的示例指标.
结论:
- 卫星图像的深度学习分析是监测经济变化的可行方法.
- 开发的框架为经济分析和政策制定提供了有价值的,自动化的见解.
- 可访问的代码和数据有助于进一步的研究和应用.
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