Neural Network

A Neural Network is a computational model composed of interconnected layers of artificial neurons, or nodes, that process information by transmitting signals through weighted connections. Each neuron receives inputs, applies a weighted sum and an activation function, and passes the result to subsequent layers. Through training on labeled data, the network adjusts its weights using backpropagationBackpropagationBackpropagation is the fundamental algorithm for computing gradients in neural network training, propagating error si... and gradient descent to minimize prediction errors. This learning process enables neural networks to approximate complex nonlinear functions, making them universal function approximators. Neural Network Architectures in Geospatial AIThe field of geospatial AI employs diverse neural network architectures tailored to different data types. Feedforward networks process tabular geospatial features for tasks like property valuationProperty ValuationProperty Valuation is the process of estimating the monetary value of real estate based on location, physical charact... and demand prediction. Convolutional Neural Networks (CNNs) excel at processing satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... for classification and feature extraction. Recurrent Neural Networks (RNNs) and their variants handle time-series data for crop growth monitoring and climate prediction. Graph Neural Networks model spatial relationships in network data like road systems and utility grids. The choice of architecture depends on whether the geospatial dataGeospatial DataGeospatial data encompasses information about the location, shape, and relationships of physical features on Earth. I... is structured as images, sequences, graphs, or tabular features. Capabilities and Practical ConsiderationsNeural networks can learn features directly from raw data, eliminating the manual feature engineering required by traditional algorithms. They scale to massive datasets and benefit from GPU acceleration for parallel computation. However, they require substantial training data, significant computational resources, and careful architecture design. Overfitting is a constant concern, mitigated through techniques like dropoutDropoutDropout is a regularization technique that randomly deactivates neurons during training, preventing neural networks f..., regularizationRegularizationRegularization encompasses techniques that prevent machine learning models from overfitting to training data, ensurin..., and early stopping. Interpretability remains a challenge, as the learned representations are not easily human-readable, though explainability methods are improving.