AI-Enhanced UAV Photogrammetry Point Clouds for Cultural Heritage Assessment

Advancements in low-altitude remote sensing and image analysis have revolutionized the digitisation of real-world objects, initially represented as point clouds. Over the past decade, drone-based surveying has gained traction; however, noise during data capture and 3D reconstruction remains a critical challenge, affecting the accuracy and usability of UAV images in real-world applications. This study presents a novel approach to enhance cultural heritage assessment using deep learning models based on the Autoencoder Gaussian Mixture Clustering Model and the Autoencoder K-means Clustering Model. These models improve the accuracy of point clouds generated from UAV images for better condition assessment and survey. The research focuses on photogrammetric accuracy parameters for clustering point clouds. The new approach chooses K-means for finding global patterns due to its robust accuracy, and the Gaussian Mixture Clustering Model for local changes and inspection applications. Moreover, these models investigate the accuracy of point cloud clusters generated from K-means and the Gaussian Mixture Clustering Model. To validate the new approach, the study evaluates variations in key parameters under diverse built-environment conditions using the Temple of Neptune point cloud in Paestum, Italy. The results demonstrate significant improvements in point cloud 3D reconstruction, leading to more accurate surveys and assessments. The proposed method surpasses traditional techniques, offering enhanced applicability for point-cloud filtering. Furthermore, comparisons of clustering results highlight the algorithm, establishing its promising potential for advancing UAV-based surveying and inspection practices.