Webpage segmentation is a crucial task in web analysis, enabling improvements in information retrieval, user experience, and automated web understanding.However, existing segmentation datasets often lack both comprehensive visual and textual segmentation, as well as classification systems that capture the functional and qualitative aspects of webpages.In this paper, we introduce a novel webpage segmentation dataset that addresses these gaps by providing both visual and textual segmentations, alongside two classification frameworks.The first framework defines a nominal classification of segments based on their functional roles, such as main content, header, footer, and navigation bar.The second introduces an ordinal classification assessing the digital maturity of webpage segments, offering a structured evaluation of their design evolution and complexity.By integrating both classification schemes into a single dataset, our approach enables a more holistic analysis of webpage structures.Furthermore, given the rapid evolution of web design conventions, content structures, and technological trends, our dataset is designed to reflect contemporary webpage characteristics, ensuring its relevance for modern applications in web analysis and machine learning.Additionally, we provide first results on visual segmentation, demonstrating the effectiveness of our dataset in practical applications.