**1. Introduction**

With the increasing complexity of the buildings in highly urban areas since the late 90s, 3D Cadastre has been a subject of interest. 3D Cadastre is beneficial for land registries, architects, surveyors, urban planners, engineers, real estate agencies, etc. [1]. On one hand, it shows the spatial extent of the ownership and, on the other, it facilitates 3D property rights, restrictions, and responsibilities [2,3]. However, for realization of the 3D Cadastre concept, there is no one single solution. User needs, the national political and legal situation, and technical possibilities should be taken into account. This was also clear from the International Federation of Surveyors (FIG) questionnaire completed by many countries in 2010 [4,5]. In recent years, many 3D Cadastre activities have been initiated worldwide, since 3D information is essential for efficient land and property management [6–9]. An investigation into the legal foundation has been done for 15 countries covering Europe, North and Latin America, the Middle East, and Australia [10], not only overground, but also underground [11]. However, there is still no fully implemented 3D Cadastre in the world [4] due to a lack of integration between legal, institutional, and technical parties involved. With the technical developments, physical and legal

representation for the purposes of 3D Cadastre are being actively researched; however, considering the dynamics of the complex relationships between people and their properties, we must take into account the time aspect, which needs more attention [12]. 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 yet been explored. Therefore, the current paper aims to investigate the opportunities provided by automatic techniques for detecting changes based on point clouds in support of 3D indoor Cadastre. When using the term "automatic", we mean that the process of change detection and separation of permanent changes from temporary changes are automatic. However, setting relevant parameters for each step is required by an expert. Moreover, Cadastral expert intervention is required to connect the land administration database, if it exists, to the physical space subdivision extracted from point clouds. The remainder of this section includes the relevance of our research, showing a real example in The Nederlands and related scientific work in the field.

In recent years, many examples can be found of changes in the functionality of buildings. According to the statistics shared by Rijksolverheid [13] in The Netherlands, 17% of the commercial real estate is empty. The Ministry of Interior and Kingdom Relations (BZK) and the Association of Dutch Municipalities (VNG) set up an expert team to support municipalities in the transformation of empty buildings from commercial to residential use. One of the examples is a nursing home located in the city of Hoorn (Figure 1a), 40% of which was owned by housing associations and 60% by health care organizations and was changed in 2015 into student accommodation and privately owned apartments (Figure 1b).

**Figure 1.** (**a**) Changing from a nursing house (**top**) to student accommodation (**middle**) and (**b**) privately owned apartments (**bottom**) [13].

From the recent research in the field, it was observed that point clouds are a valuable source for decision makers in the domain of urban planning and land administration. Laser scanner data acquired with aerial laser scanners (ALS), mobile laser scanners (MLS), and terrestrial laser scanners (TLS) have been used for reconstruction of 3D cities, building facades, roof reconstruction [14–16], and damage assessment of the buildings before and after a disaster [17]. In the domain of forestry, point clouds are used for monitoring the growth of trees and changes in the forest canopy. Xiao et al. [18] used point clouds to monitor the changes of trees in urban canopies. Regarding buildings, some methods combine images with laser scanner data for facade reconstruction [19–21]. There has been incredible progress in recent years in the automation of 3D modeling based on point clouds [22–24] and more specifically in subdividing the space to semantic subdivisions, such as offices, corridors, staircases, and so forth [25–27]. Challenges for detecting changes for updating 3D Cadastre in an urban environment using ALS and image-based point clouds for 3D Cadastre were also explored [28]. Regarding indoor spaces, geometric changes during the lifetime of a building were analyzed for the Technical University of Munich (TUM) [29], as shown in Figure 2; however, they were not related to Cadastre. This fact motivates us to use point clouds and monitor changes for updating 3D Cadastre.

**Figure 2.** Geometric changes of indoor spaces during the lifetime of a building for complex buildings for the Technical University of Munich (TUM). Campus 3D map-rendered corridors [29].

From a technical point of view, the three possibilities to detect geometric changes over time are:


In the current paper, we are using the third option becausepoint clouds are used for change detection and representation of the 3D Cadastre because they reflect more detail of the environment and they are close to the current state of the building. Furthermore, it is easy to convert the point clouds to other data representation forms, such as vector and voxel, for usage in 3D Cadastre models [30]. Having more than one point cloud dataset as an input information change detection can be done either in a low level of detail and just based on the geometry, or in a higher level of detail by interpretation of the geometry to semantics. The changes between two epochs could be due to differences in the furniture and not the permanent structure, which needs a higher level of interpretation from point clouds. However, only comparing the geometry of two point clouds is not sufficient to interpret 3D Cadastre related changes. Additionally, we need to have an understanding of the spaces inside the buildings to relate them to 3D spatial units in a 3D Cadastre model and properly register them in a database.

In the domain of Cadastre, there is a need to subdivide the spatial units vertically and have a 3D representation in 3D spatial databases. Van Oosterom discusses different types of data representation for 3D model storage. including voxels, vectors, and point clouds [30]. The flexibility of point clouds in conversion to voxel or vector formats makes it easier to use point clouds in Cadastre. Additionally, point clouds can represent the 3D details of the buildings from inside and outside. From the standards and modelling aspects, researchers have developed models to provide a common framework for 3D Cadastre. The main international framework for 3D Cadastre is the Land Administration Model (LADM) [31]. However, in LADM there is a lack of connection between spatial models, such as Building Information Models (BIM) and IndoorGML. Oldfield et al. [32] try to fill this gap by enabling the registration of spatial units extracted from BIM into a land administration database. Aien et al. [1] study the 3D Cadastre in relation to legal issues and their physical counterparts. The authors introduce a 3D Cadastral Data Model (3DCDM) to support the integration of physical objects linked with the legal objects into a 3D Cadastre. Another application of LADM is for using the access rights for indoor navigation purposes. The access rights of spatial units is defined in the LADM and could be connected to IndoorGML for customized navigation in the spatial units [33]. Another model that builds on LADM for supporting the 3D spatial databases in terms of land administration was developed by Kalantari et al. [34]. The authors propose strategies for the implementation of the 3D National Digital Cadastral Database (3D-NDCDB) in Malaysia. The proposed database gives instructions for cadastral data collection, updating the data and storage. Their database is a one-source 3D database which is compliant with the LADM. Other researchers discuss the need for new spatial representations and profiles (e.g., a point clouds profile for non-topological 3D parcels) [35,36]. Atazadeh et al. investigate the integration of legal and physical information based on international standards [37].

It is challenging to automatically link the right spaces to the 3D Cadastre and database. For this task, each space subdivision can represent a spatial unit or a group of spatial units in a building. These spatial units, to some extent, are supported in LADM through four main classes: LA\_Party, LA\_RRR, LA\_BAUnit, and LA\_SpatialUnit [38]. From the point of view of changes in indoor spaces LA\_SpatialUnit, which represents legal objects, and LA\_RRR, which represents rights, restrictions, and responsibilities, are the interesting classes. The reason that we decided to use the LADM for our experiments is that it is more complete and recent than other cadastral data models, such as the Federal Geographic Data Committee (FGDC) (Cadastral Data Content Standard—Federal Geographic Data Committee) [39], DM01 [40], and The Legal Property Object Model [41]. Additionally, unlike other cadastral data models that are based on 2D land parcels, LADM suggests modeling classes for 3D objects [1]. However, there is a lack of support for 3D Cadastre in terms of data representation

and spatial operations in the current 3D Cadastre models, such as LADM. For example, Cadastre parcels are mainly represented as 2D parcels, while, in a multi-storey building, there is a need to show the property as a volumetric object. The only class for supporting 3D spatial units in the LADM is the Class LA\_BoundaryFace, which uses GM\_MultiSurface to model 3D objects. The problem of GM\_MultiSurface is that it is not sufficient for 3D spatial analysis and representation [1]. To compensate for this shortage in our workflow, enriched point clouds were used as an external database to store and represent the 3D objects. Using attributed point clouds enabled us to calculate necessary spatial attributes for 3D Cadastre.

Currently, there is no framework or standard for connecting point clouds, 3D models, and the LADM. Therefore, in this paper, we propose such workflow based on experiments on two different datasets. One example is of a commercial building, of which the point clouds are acquired using two different MLS systems before and after renovation. In addition, one more example of a building captured at different moments with TLS will be shown. This research shows the usage of point clouds as a primary and final format of data representation to enrich the 3D Cadastre. The remainder of this article explains the used methodology and the obtained results, followed by critical discussion and conclusions with a shared view on the way forward.
