Planning and collection of ground truth data is a critical component of remote sensing and image interpretation, serving as the foundation for accurate analysis and validation of remotely sensed information. Ground truth refers to reference data collected on the earth’s surface that accurately represents the real-world conditions of the area being studied. This data is essential for calibrating, validating, and interpreting satellite images, aerial photographs, or other remotely sensed data. Proper planning ensures that the data collected is representative, reliable, and suitable for the specific objectives of the geoinformatics study. The planning process begins with defining the objectives of the ground truth data collection. These objectives vary depending on the type of remote sensing study, such as land use/land cover mapping, vegetation analysis, hydrological studies, or urban planning. Once the objectives are clear, the next step is to select suitable study areas and sampling locations. Sampling design is crucial and can follow systematic, random, or stratified approaches, depending on the spatial variability of the target features and the required accuracy. Stratified sampling is often preferred in heterogeneous areas, as it ensures that all significant