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Nowcasting Air Quality by Fusing Insights from Meteorological Data, Satellite Imagery and Social Media Images Using Deep Learning

机译:使用深度学习融合气象数据,卫星图像和社交媒体图像的洞察力,即时播报空气质量

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Peatland fire and haze events in Southeast Asia are disasters with trans-boundary implications, having increased in recent years along with rapid deforestation, land clearing and severe dry seasons. Aerosols are emitted in high concentrations from the fires, which degrade air quality and reduce visibility, in turn causing economic, social, health, and environmental problems. During haze events, it is critical for public authorities to have timely information about affected populations. Currently, Indonesian disaster management authorities manage forest and peatland fire and haze events based on satellite data and sensors. They are looking for more real-time information in order to better protect vulnerable populations and environment. This paper explores information on visibility extracted from photos shared on social media to improve forecasting performance for haze severity. Our results show that visibility information can improve forecast accuracy over a baseline approach with common features, namely data from satellites and ground air quality sensors. Furthermore, by using social media photos, our model adds a near real-time property to the forecast model, with potential to improve disaster management and mitigation.
机译:东南亚的泥炭地火灾和阴霾事件是具有跨边界影响的灾害,近年来,随着迅速的森林砍伐,土地清理和严峻的干旱季节,这种情况有所增加。火灾以高浓度排放气溶胶,这会降低空气质量并降低能见度,进而引起经济,社会,健康和环境问题。在霾事件期间,公共机构及时获得有关受影响人群的信息至关重要。目前,印度尼西亚的灾害管理机构根据卫星数据和传感器来管理森林和泥炭地的火灾和霾事件。他们正在寻找更多的实时信息,以更好地保护脆弱的人群和环境。本文探讨了从社交媒体上共享的照片中提取的可见性信息,以提高对霾严重度的预测性能。我们的结果表明,能见度信息可以通过具有共同特征的基线方法(即来自卫星和地面空气质量传感器的数据)提高预报的准确性。此外,通过使用社交媒体照片,我们的模型将近实时属性添加到了预测模型中,具有改善灾难管理和缓解的潜力。

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