Land Cover Classification

Land cover classification assigns discrete categorical labels to areas of Earth's surface based on their physical and biological characteristics as observed by remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... instruments. Unlike land use (which describes human purpose), land cover describes the actual biophysical material present, such as deciduous forest, irrigated cropland, impervious surface, or open water. Classification schemes range from simple binary maps to hierarchical systems with dozens of classes. Approaches and methodsClassification can be performed using supervised methods (maximum likelihood, random forestRandom ForestRandom Forest is an ensemble machine learning method that builds multiple decision trees during training and merges t..., support vector machines) trained on labeled reference data, unsupervised clustering algorithms (K-means, ISODATA), or deep learning architectures (U-NetU-NetU-Net is an encoder-decoder neural network architecture with skip connections designed for precise image segmentation..., convolutional neural networks). Object-based image analysis (OBIA) groups pixels into meaningful segments before classification, reducing noise and incorporating spatial context. Global and national programsMajor land cover products include the USGS National Land Cover Database (NLCD), ESA WorldCover, the Copernicus Global Land Service, and the GlobeLand30 dataset. These products are updated periodically and provide essential baselines for tracking environmental change, reporting on Sustainable Development Goals, and modeling climate impacts. Land cover classification is one of the most fundamental applications of remote sensing, transforming raw imagery into actionable maps that inform policy, conservation, and development decisions at local to global scales.