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.