  This paper presents a novel approach in wildfire prediction through the
integration of multisource spatiotemporal data, including satellite data, and
the application of deep learning techniques. Specifically, we utilize an
ensemble model built on transfer learning algorithms to forecast wildfires. The
key focus is on understanding the significance of weather sequences, human
activities, and specific weather parameters in wildfire prediction. The study
encounters challenges in acquiring real-time data for training the network,
especially in Moroccan wildlands. The future work intends to develop a global
model capable of processing multichannel, multidimensional, and unformatted
data sources to enhance our understanding of the future entropy of surface
tiles.
