Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Battina, Subhadha; Jaganathan, Siva Title A name given to the resource AIs Role in Semantic Segmentation for Data-Driven 3D Models of Heritage Structures Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Civil Engineering;Volume;591 LNCE;pp.793-808 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4051-5_77" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4051-5_77</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003633889?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003633889?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Battina S., Christ University, Bengaluru, India; Jaganathan S., Christ University, Bengaluru, India Description An account of the resource Using point cloud data from laser scanning and photogrammetry to create three-dimensional models with scan-to-BIM processes has become increasingly common in heritage conservation. During the processing of point clouds, semantically segmenting data can translate captured spatial information into intelligent data structures, enabling classified, accurate, data-driven digital models of heritage structures. Subsequently, digital models are utilized for analytical tasks like structural tests, energy optimization, etc. Artificial Intelligence (AI) has become a promising solution for automating Three-Dimensional Point Cloud Semantic Segmentation (3DPCSS), enabling a faster and more accurate composition of parametric objects within 3D modeling and management systems. However, implementing 3DPCSS solely with AI presents various technical and theoretical challenges. The geometrical complexities inherent in historical structures often necessitate manual segmentation processes or oversimplified representations that miss the unique characteristics of the building. Therefore, selecting an appropriate AI framework for 3DPCSS is essential to ensure accurate results. Multiple factors determine algorithms selection, making it challenging to categorize universal solutions. The paper highlights the key factors: 1) Data collecting tools and technologies, 2) Types of the dataset, 3) Complexity of geometrical elements, and 4) Computational tasks. AI frameworks are typically selected based on the suitability and significance of these factors relative to the projects intent. Very few studies talk about the choices of algorithms. This papers significant contribution is recognizing trends in effective data acquisition strategies through a case study in India. Additionally, it identifies state-of-the-art AI models from the past decade based on a systematic literature study. The paper infers the extensive use and advancement of hybrid approaches tailored to multi-modal data types and the specific needs of heritage projects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource 3D Point Cloud Semantic Segmentation (3DPSS); AI in cultural heritage; Data-driven 3D Modeling; Digital Heritage; Scan-to-BIM Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23662557; ISBN: 978-981964050-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online