Principles of Convolution-Recurrent Deep Model Constructing to Predict Spatial-Temporal Processes
The article examines the structure and trains a convolutional-recurrent neural network model to solve the problem of high-precision forecasting of spatial-temporal processes using the example of forest fire detection. The aim of the work is to build a deep neural network architecture that simultaneously integrates spectral and spatial information. The network has several modules (layers): an input module, to the input of which date and information about weather conditions are supplied, a convolutional module for processing the processes of spatial and spectral dependencies on these pixels, an output module that returns the forecast result.