Classification:

Classification is a fundamental spatial data analysis technique wherein data is grouped into distinct classes or categories based on specific attributes or characteristics. In geospatial contexts, this process aids in thematic mapping and data organization. For instance, satellite imagery can be classified into land cover classes like forests, water bodies, and urban areas, facilitating resource management and environmental studies.

Supervised Image Classification:

Supervised image classification is a sophisticated geospatial method employed to train algorithms in recognizing objects or land cover types within imagery. This process involves the selection of training samples with known class labels to guide the algorithm. Complex algorithms such as Maximum Likelihood or Support Vector Machines then utilize spectral signatures to classify all pixels in the image. This precision is invaluable in geospatial applications such as land use planning, disaster assessment, and natural resource management, where accurate spatial information is critical for decision-making.