Random Forest

Random Forest is a supervised ensemble learning algorithm that constructs a multitude of decision trees at training time and outputs the mode of their predictions for classification or the mean prediction for regression. Each tree is trained on a random subset of the data using bootstrap sampling, and at each split only a random subset of features is considered. This dual randomness reduces overfitting and variance compared to individual decision trees, producing robust models that generalize well to unseen data. How Random Forest Works in Geospatial ApplicationsIn geospatial contexts, Random Forest is commonly applied to classify satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... into land cover categories such as water, forest, urban, and agricultural land. Each pixel or image patch is described by a feature vector derived from spectral bands, vegetation indices, texture measures, and ancillary data like elevation or slope. The algorithm trains hundreds of decision trees, each using a different random sample of training pixels and feature subsets. During prediction, each tree votes for a class, and the majority vote determines the final classification. Feature importance scores help analysts understand which spectral bands or indices are most useful for distinguishing land cover types. Advantages and Limitations in Spatial AnalysisRandom Forest handles high-dimensional feature spaces well, making it effective for multi-spectral and hyperspectral imagery with dozens of bands. It requires minimal hyperparameter tuningHyperparameter TuningHyperparameter Tuning is the process of optimizing the configuration parameters that control model training and archi..., resists overfitting through its ensemble approach, and provides built-in estimates of feature importance and prediction uncertainty. However, Random Forest can struggle with highly imbalanced classes, may produce less smooth classification maps than deep learning approaches, and its computational cost grows linearly with the number of trees. For very large remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... datasets, training can become memory-intensive.