Long Short-Term Memory (LSTM)

Long Short-Term Memory networks are a specialized type of recurrent neural networkNeural NetworkA Neural Network is a computing system inspired by the structure of biological neural networks in the brain. It forms... introduced by Hochreiter and Schmidhuber to address the difficulty of learning long-range dependencies in sequential data. LSTMs use a gating mechanism consisting of input, forget, and output gates that control the flow of information through a memory cell. The forget gate decides what information to discard from the cell state, the input gate determines what new information to store, and the output gate controls what information to pass to the next time step. This architecture allows LSTMs to selectively remember or forget information over hundreds of time steps. Time-Series Geospatial AnalysisGeospatial AnalysisGeospatial analysis applies statistical methods and specialized software to interpret spatial data, uncovering patter... with LSTMsLSTMs excel at geospatial tasks involving temporal sequences. Multi-temporal satellite image analysis uses LSTMs to model vegetation growth cycles for crop type classification and yield prediction. Urban mobilityUrban MobilityUrban mobility aims to optimize transportation systems in cities, enhancing efficiency, reducing congestion, and impr... applications employ LSTMs to forecast traffic flow, predict transit demand, and estimate arrival times from historical patterns. Climate modeling uses LSTMs to predict temperature, precipitation, and extreme weather events from long historical records. Water resource management applies LSTMs to forecast river discharge, reservoir levels, and groundwater changes. Air quality monitoring combines spatial sensor data with LSTM temporal modeling for pollutant concentration forecasting. Comparison with Alternatives and Current RelevanceWhile Transformers have supplanted LSTMs for many NLP tasks, LSTMs remain competitive and practically relevant for geospatial time series where sequence lengths are moderate and computational resources are limited. LSTMs consume less memory than attention-based models for long sequences and are well-suited to streaming data scenarios. Bidirectional LSTMs process sequences in both directions for improved context understanding. ConvLSTM variants combine convolutional and recurrent processing for spatiotemporal data like video and multi-temporal imagery grids.