Machine Learning based literature review of Land Administration Domain Model (LADM): a structural topic modelling approach

• Published in: Boletim de Ciências Geodésicas Vol. 31; pp. 1 - 23
• Main Authors: Mehmood, Usman, Ujang, Uznir, Azri, Suhaibah
• Format: Journal Article
• Language: English; Portuguese
• Published: Curitiba Universidade Federal do Paraná, Centro Politécnico 01.01.2025
• Subjects: Collaboration; Data integration; Datasets; Interdisciplinary aspects; Interoperability; Keywords; Land management; Land use planning; Literature reviews; Machine learning; Marine technology; Metadata; Modelling; Semantics; Sustainability management; Sustainable development; Trends; Kenya; China
• ISSN: 1413-4853; 1982-2170; 1982-2170
• DOI: 10.1590/s1982-21702025000100006

The Land Administration Domain Model (LADM) standardizes land management by integrating legal, spatial, and administrative information. This study examines LADM-related research using Structural Topic Modelling (STM) on 199 publications (2008-2024). Seven dominant topics emerged: land administration systems, property valuation, 3D cadastral modelling, LADM extensions, building and spatial rights, cadastral systems, and land object modelling. Key findings highlight sustained interest in spatial modelling, legal frameworks, and cadastral data integration, alongside emerging trends such as country-specific LADM profiles (e.g., China, Kenya, Malaysia) and technological advancements like BIM and marine georegulation models. Challenges persist in data complexity, semantic interoperability, and 4D cadastres. The study recommends expanding semantic models, fostering interdisciplinary collaboration, and developing tailored national profiles to enhance LADM's applicability and promote sustainable land management practices globally.