5 research outputs found

    Deep Learning Model for Global Spatio-Temporal Image Prediction

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    Mathematical methods are the basis of most models that describe the natural phenomena around us. However, the well-known conventional mathematical models for atmospheric modeling have some limitations. Machine learning with Big Data is also based on mathematics but offers a new approach for modeling. There are two methodologies to develop deep learning models for spatio-temporal image prediction. On these bases, two models were built—ConvLSTM and CNN-LSTM—with two types of predictions, i.e., sequence-to-sequence and sequence-to-one, in order to forecast Aerosol Optical Thickness sequences. The input dataset for training was NASA satellite imagery MODAL2_E_AER_OD from Terra/MODIS satellites, which presents global Aerosol Optical Thickness with an 8 day temporal resolution from 2000 to the present. The obtained results show that the ConvLSTM sequence-to-one model had the lowest RMSE error and the highest Cosine Similarity value. The advantages of the developed DL models are that they can be executed in milliseconds on a PC, can be used for global-scale Earth observations, and can serve as tracers to study how the Earth’s atmosphere moves. The developed models can be used as transfer learning for similar image time-series forecasting models

    A Comprehensive survey on deep future frame video prediction

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    El present projecte planteja l'estudi comprensiu i extens per a la tasca de predicció de fotogrames donada una seqüència de vídeo. Mitjançant l'anàlisi de l'estat de l'art en generació d'imatges, xarxes convolucionals i adversàries l'objectiu és establir les forces i utilitats d'aquesta tasca
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