Differential Impacts of Very High-Resolution Terrain Data and Multispectral Imagery on the Accuracy of Land-Use Classification Using Machine Learning

Accurate land-use and land-cover (LULC) classification is essential for urban planning, environmental monitoring, and resource management. With the availability of high-resolution remote sensing, mapping urban areas has improved; however, selecting effective data and algorithms remains challenging. This study examines the impact of combining very high-resolution orthophotos with elevation datasets, including the Digital Surface Model (DSM), Digital Terrain Model (DTM), and normalized DSM (nDSM), on supervised machine learning classifiers. Data was acquired using a DJI Matrice 350 RTK drone at 5 cm resolution, then resampled to 2 m for efficiency. Three classifiers, Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting Classifier (GBC), were tested in two stages: first with orthophotos only, then with added elevation features. The SVC model, in particular, achieved the highest overall accuracy (91.43%) and F1-score (90.75%), excelling at distinguishing between spectrally similar classes such as buildings and tarred roads. Elevation features helped distinguish spectrally similar classes, such as buildings and tarred roads, reducing misclassification common in RGB-only models. The findings highlight that integrating spectral and elevation data enhances classification reliability, as orthophotos provide color and texture while elevation adds structural detail. This fusion approach offers a scalable and high-precision method for urban mapping and environmental analysis.