JOURNAL ARTICLE

Towards 3D Indoor Cadastre Based on Change Detection from Point Clouds

Mila KoevaS. NikoohematSander Oude ElberinkJavier MoralesC. LemmenJ.A. Zevenbergen

Year: 2019 Journal:   Remote Sensing Vol: 11 (17)Pages: 1972-1972   Publisher: Multidisciplinary Digital Publishing Institute

Abstract

3D Cadastre models capture both the complex interrelations between physical objects and their corresponding legal rights, restrictions, and responsibilities. Most of the ongoing research on 3D Cadastre worldwide is focused on interrelations at the level of buildings and infrastructures. So far, the analysis of such interrelations in terms of indoor spaces, considering the time aspect, has not been explored yet. In The Netherlands, there are many examples of changes in the functionality of buildings over time. Tracking these changes is challenging, especially when the geometry of the spaces changes as well; for example, a change in functionality, from administrative to residential use of the space or a change in the geometry when merging two spaces in a building without modifying the functionality. To record the changes, a common practice is to use 2D plans for subdivisions and assign new rights, restrictions, and responsibilities to the changed spaces in a building. In the meantime, with the advances of 3D data collection techniques, the benefits of 3D models in various forms are increasingly being researched. This work explores the opportunities for using 3D point clouds to establish a platform for 3D Cadastre studies in indoor environments. We investigate the changes in time of the geometry of the building that can be automatically detected from point clouds, and how they can be linked with a Land Administration Model (LADM) and included in a 3D spatial database, to update the 3D indoor Cadastre. The results we have obtained are promising. The permanent changes (e.g., walls, rooms) are automatically distinguished from dynamic changes (e.g., human, furniture) and are linked to the space subdivisions.

Keywords:
Cadastre Point cloud Computer science Point (geometry) Subdivision Architectural engineering Work (physics) Remote sensing Land administration Data science Environmental planning Geography Civil engineering Cartography Computer vision Geometry Engineering

Metrics

25
Cited By
2.43
FWCI (Field Weighted Citation Impact)
53
Refs
0.87
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

3D Modeling in Geospatial Applications
Physical Sciences →  Engineering →  Building and Construction
Remote Sensing and LiDAR Applications
Physical Sciences →  Environmental Science →  Environmental Engineering
3D Surveying and Cultural Heritage
Physical Sciences →  Earth and Planetary Sciences →  Geology

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