The Extraction of BIM/IFC Model for LADM Legal Spaces

Abstract. Strata management in Malaysia faces persistent challenges due to delays in strata title issuance, fragmented data systems, and inefficiencies in legal space administration. Integrating Building Information Modelling (BIM) with the Land Administration Domain Model (LADM) offers a structured approach to addressing these issues. This study explores the extraction of Industry Foundation Classes (IFC) elements from BIM models to enhance the representation of legal spaces within the Malaysian LADM country profile. The research focuses on mapping key IFC elements such as IfcSpaces, IfcBuildingElements, and ownership attributes to LADM classes for strata title registration. Using Google Colab for data extraction and Enterprise Architect for schema mapping, the methodology ensures seamless integration between spatial and legal property data. The results demonstrate the potential of BIM-IFC in supporting 3D property rights visualisation and legal space definition, contributing to a more efficient, transparent, and data-driven approach to strata property management.

Authors

Institutions

Publication Details

Journal
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Published
2026-01-15
DOI
https://doi.org/10.5194/isprs-archives-xlviii-4-w17-2025-345-2026
Primary Topic
3D Modeling in Geospatial Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Extraction of BIM/IFC Model for LADM Legal Spaces

Ainn Zamzuri, Alias Abdul Rahman, Muhammad Imzan Hassan, Mehmet Alkan
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
3D Modeling in Geospatial Applications
article

The Extraction of BIM/IFC Model for LADM Legal Spaces

Ainn Zamzuri, Alias Abdul Rahman, Muhammad Imzan Hassan, Mehmet Alkan
article en

Abstract

Abstract. Strata management in Malaysia faces persistent challenges due to delays in strata title issuance, fragmented data systems, and inefficiencies in legal space administration. Integrating Building Information Modelling (BIM) with the Land Administration Domain Model (LADM) offers a structured approach to addressing these issues. This study explores the extraction of Industry Foundation Classes (IFC) elements from BIM models to enhance the representation of legal spaces within the Malaysian LADM country profile. The research focuses on mapping key IFC elements such as IfcSpaces, IfcBuildingElements, and ownership attributes to LADM classes for strata title registration. Using Google Colab for data extraction and Enterprise Architect for schema mapping, the methodology ensures seamless integration between spatial and legal property data. The results demonstrate the potential of BIM-IFC in supporting 3D property rights visualisation and legal space definition, contributing to a more efficient, transparent, and data-driven approach to strata property management.

˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesVol. XLVIII-4/W17-2025
Yıldız Technical University (TR), University of Technology Malaysia (MY)
Universiti Teknologi Malaysia
Peace, Justice and strong institutions
Openalex Percentile: Top 87%
3D Modeling in Geospatial Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.