… to extract the spatial and temporal features of atmospheric … that the characteristics of the
data are multi-features, which … preprocesses the data, merges the features of the data into a …

… In this paper, a CNN-LSTM model based on spatial-temporal trajectory topology is … First,
input data is transformed into space-time track topology map. On the one hand, the space-time …

… of frame-levels, and then employ a SiameseLSTM architecture for spatial-temporal fusion …
based temporal network. We evaluate the performance of the proposed CNN-RNN based …

… CNN lies in the capture to spatial features. … LSTM-MFCN focuses on both spatial and temporal
features of large multi-scales, producing more comprehensive and thorough grasp to time …

… In this paper, a novel model based on CNN and LSTM (long-short term memory) was … This
study proposes a CNN-LSTM model architecture that integrates spatial and temporal features …

… effectively combine both spatial and temporal features for crop … spatial features of crops and
the extracted spatial features … LSTM model that can effectively process temporal features. To …

… (CNN) and long short-term memory (LSTM) hybrid model (CNN-LSTM), the spatial-temporal
feature … By building the CNN-LSTM model, the spatial-temporal features in datasets could be …

… models were single and taken either spatial features or temporal features. Some of the hybrid
… CNN and LSTM to capture spatial and temporal features of road network for single station. …

… A novel framework for combining CNN and LSTM is suggested. This framework combines
the temporal features of LSTM and CNN to perform action recognition. The proposed …

… CNN for this analysis: (1) Extracting spatial features: CNN … learning approach combining
CNN and LSTM in traffic data … the spatial and temporal features of traffic images and time series …