Wildfire Monitring Based on LSTM and Deep Learning

Satellite detection has been an advantageous method in wildfire detection for its near-real-time monitoring and large coverage. In this paper, a time scale base wildfire detection is proposed based on himawari-8 satellite data. Firstly, we used adjusted Otsu algorithm to remove the cloud mask. Afterwards, 7 previous data of each 10 minutes before the current time of a pixel is input into a Long Short Term (LSTM) network to predict current data. Finally, the predicted data was compared with the ground truth to determine fire. The result showed a high accuracy in prediction and better performance in determining wildfire compared with traditional dynamic threshold method. The experiment is limited by the time of the selected wildfire data and expected to test the performance over different period.