Comparative analysis of different machine learning algorithms for urban footprint extraction in diverse urban contexts using high-resolution remote sensing imagery

While algorithms have been created for land usage in urban settings, there have been few investigations into the extraction of urban footprint (UF). To address this research gap, the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities. The results indicate that pixel-based methods only excel in clear urban environments, and their overall accuracy is not consistently high. RF and SVM perform well but lack stability in object-based UF extraction, influenced by feature selection and classifier performance. Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts. SAM excels in medium-sized urban areas but falters in intricate layouts. Integrating traditional and deep learning methods optimizes UF extraction, balancing accuracy and processing efficiency. Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.