Object-Based Image Analysis (OBIA) represents a significant advancement in remote sensing and GIS, moving beyond traditional pixel-based classification to a more context-aware, object-oriented approach. The methodology focuses on grouping pixels into meaningful objects based on both spectral and spatial characteristics, allowing more accurate and interpretable land cover mapping. The process of OBIA generally involves several systematic steps. The first step is image acquisition and preprocessing. High-resolution remotely sensed data, such as multispectral or hyperspectral imagery, are collected. Preprocessing ensures that the data is suitable for analysis, which may include radiometric correction to remove sensor noise, geometric correction to align images accurately with ground coordinates, and atmospheric correction to eliminate distortions caused by atmospheric particles. This step is crucial as errors at this stage can propagate throughout the analysis. The second step is segmentation, which is the core of OBIA. In segmentation, the image is divided into homogeneous regions, called image objects,