**4. Conclusions**

In this paper, we have shown that permanent changes in buildings can be found automatically using multi-epoch mobile laser data. The detection is based on the selection of planar segments with a majority (i.e., more than 50%) of points in one epoch with a distance larger than 20 cm to the nearest

points in the other epoch. In our approach, changes are detected as dynamic changes (e.g., human, furniture) and permanent changes (e.g., walls, rooms). The permanent changes are then linked to the space subdivisions, which are extracted from the point clouds of each epoch (Section 2.2). A Cadastre expert will need to interactively group some of the space subdivision according to their legal attributes. The spaces that are changed and identified during the process will then be further analyzed to extract spatial attributes, such as boundary, area, and volume. This process can be done on point clouds where changes have occurred. Extracted spatial attributes can be exchanged between a Cadastre model, such as LADM, and the point clouds. A Cadastre expert should make decisions on updating the model according to the spatial changes. In the future, we plan to investigate the link between designed space by the architect or civil engineer and the real constructed space as measured with point clouds. This measurement is relevant for the composition of legal space in LADM, but also for building and other permits (e.g., for shops, companies, etc.). It was proved that it is also relevant for crisis management using smart indoor models in 3D [58]. Moreover, the latest updates in 3D mapping using multi-acquisition capabilities, virtual reality, and augmented reality in combination with precise architectural plans and BIM provide immense opportunities. Apart from the technical advances, our future research will be aligned with the second edition of LADM, which is currently under development and includes extensions incorporating the usage of point clouds, BIM, etc. [59].

The process of representing and linking 3D objects to the 3D Cadastre, especially for indoor use, is ongoing research. The authors of this paper hope that this work will introduce a new research avenue regarding the connection between point clouds and indoor Cadastre models.

**Author Contributions:** Conceptualization, M.K., S.N., S.O.E., J.M., C.L., and J.Z.; investigation, M.N. and S.N.; visualization, M.K., S.N., and S.O.E.; writing—original draft, M.K., S.N., S.O.E., and J.M.; review and editing, M.K., S.N., S.O.E., J.M., C.L., and J.Z.

**Funding:** The work of Shayan Nikoohemat is part of the TTW Maps4Society project Smart 3D indoor models to support crisis management in large public buildings (13742), which are (partly) financed by the Netherlands Organization for Scientific Research (NWO).

**Acknowledgments:** The authors would like to thank Yolla Al Asmar for capturing the data for the ITC building. We acknowledge the Institute of Geodesy and Photogrammetry of University of Braunschweig, Markus Gerke and his team, and Isabelle Dikhoff and Yahya Ghassoun for providing their MLS device to collect the data.

**Conflicts of Interest:** The authors declare no conflict of interest.
