Recurrent Neural Network (RNN)

A Recurrent Neural NetworkNeural NetworkA Neural Network is a computing system inspired by the structure of biological neural networks in the brain. It forms... is a class of neural networks that maintains an internal hidden state, allowing information to persist across time steps as the network processes sequences. Unlike feedforward networks that treat each input independently, RNNs pass information from one step to the next through recurrent connections, enabling them to model temporal dependencies and sequential patterns. This makes them naturally suited for data where order and context matter, such as time-series observations and movement trajectories. Geospatial Applications of RNNsRNNs process multi-temporal satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... to detect land cover changes, predict crop yields from seasonal growth curves, and forecast environmental conditions from historical climate data. Mobility analytics use RNNs to predict vehicle trajectories, estimate travel times, and model pedestrian movement patternsMovement PatternsMovement pattern analysis studies how people and vehicles travel through geographic spaces over time. By identifying ... from GPSGPSThe Global Positioning System (GPS) is a satellite-based navigation system operated by the U.S. Space Force that prov... traces. Air quality forecasting combines spatial sensor networks with RNN-based temporal modeling. Flood prediction systems use RNNs to model water level changes from precipitation and discharge time series. The sequential processing capability makes RNNs effective wherever geospatial phenomena evolve over time. Limitations and Modern AlternativesBasic RNNs suffer from the vanishing gradient problem, where gradients diminish as they propagate through many time steps, making it difficult to learn long-range dependencies. This limitation led to the development of LSTM and GRU architectures that use gating mechanisms to control information flow. More recently, TransformerTransformerThe Transformer is an attention-based neural network architecture that processes entire sequences in parallel, enabli... architectures with attention mechanisms have largely replaced RNNs for many sequential tasks due to their ability to process sequences in parallel and capture long-range dependencies more effectively. However, RNNs remain relevant for streaming applications where data arrives continuously and must be processed incrementally.