基于遥感影像大数据、卷积神经网络的福建省有效致灾雷电分布模型及应用

为了研究福建省有效致灾雷电的分布情况,基于福建省2004-2012年闪电定位数据及雷击人员伤亡数据、福建省L17级谷歌遥感影像瓦片,引入卷积神经网络模型对遥感影像所在区域是否属于人员活动的属性进行建模、训练和预测,得到福建省人员活动属性的格点产品,结合福建省历史雷电数据对有效致灾的雷电分布情况进行了分析,结果表明:①设...

Study and Application of Effective Disaster-Causing Lightning Distribution in Fujian Province Based on Remote Sensing Image Sensing and Convolutional Neural Network

In order to study the distribution of effective disaster causing lightning in Fujian Province, based on the lightning location data and lightning casualty data of Fujian Province in 2004-2012, and the L17 class Google remote sensing image tiles of Fujian Province, the Convolutional Neural Network (CNN) model is introduced to model, train, and predicts for identifying whether the area where the remote sensing image belongs to is unpopulated. We obtained the grid products of the activity attribute of Fujian Province, combining with the historical lightning data of Fujian Province and analyzed the actual distribution of lightning. The results show that: (1) The designed remote sensing image and CNN identification model had certain feasibility and accuracy, passed the hypothesis test with a significance level of 0.01. (2) 63.55% of the grid points in Fujian Province were in unpopulated areas. (3) An average of 45.36% of lightning fell in unpopulated areas, and early warning and prediction of other disaster affecting lightning was a feasible way to improve the effectiveness of emergency mitigation services according to local conditions. (4) The correlation between the effective lightning density and the historical lightning casualty data was much greater than that of the conventional lightning density and the historical lightning casualty data, and the distribution of effective lightning was more indicative than the regular lightning distribution.