Comparative Study of Normalization Methods for DEMNAS Elevation Data to Grayscale Representation for 3D Terrain Visualization

Abstract This study presents a comprehensive comparative analysis of seven normalization techniques applied to DEMNAS (Digital Elevation Model Nasional) elevation data for grayscale image conversion, essential in 3D terrain visualization and 3D printing. The methods compared are Min-Max Scaling, Z-Score Scaling, Logarithmic Transformation, Square Root, Power (Gamma), Sigmoid, and Arctangent normalization. Using QGIS and Python, elevation data were transformed into grayscale images with pixel values ranging from 0 to 255, representing topographic variation. Visual, statistical, and histogram-based analyses reveal that Min-Max scaling and Arctangent normalization provide the most balanced grayscale distribution and topographic contrast. Logarithmic and Gamma methods emphasize lower elevations, while Z-Score and Sigmoid are more centered around mean values. This study confirms that normalization choice significantly affects terrain visualization quality and should be tailored to specific data characteristics and application contexts.