Optimising Floor Plan Extraction: Applying DBSCAN and K-Means in Point Cloud Analysis of Valencia Cathedral

Accurate documentation of the geometry of historical buildings presents a considerable challenge, especially when dealing with complex structures like the Metropolitan Cathedral of Valencia. Advanced technologies such as 3D laser scanning has enabled detailed spatial data capture. Still, efficient handling of this data remains challenging due to the volume and complexity of the information. This study explores the application of clustering techniques employing Machine Learning-based algorithms, such as DBSCAN and K-means, to automate the process of point cloud analysis and modelling, focusing on identifying and extracting floor plans. The proposed methodology includes data geo-referencing, culling points to reduce file size, and automated floor plan extraction through filtering and segmentation. This approach aims to streamline the documentation and modelling of historical buildings and enhance the accuracy of historical architectural surveys, significantly contributing to the preservation of cultural heritage by providing a more efficient and accurate method of data analysis.