Abstract:
Remote sensing data is a rich resource of information, as it provides a time-wise sequence
of data, and therefore can be used for prediction purposes. In this paper, we addressed the challenge of using
time series on satellite images to predict the Glacial Lake Outburst Flood(GLOF). In order to predict GLOF,
we proposed two-step approach. In the first step, our aim is to extract the pixel-wise information about water,
snow, and soil at different time stamps and prepare them for use in the training input. The second step we use
is Long Short Term Memory (LSTM) network in order to learn temporal features and thus predict the future
pixel value of water, snow, and soil.
