# An Indoor Space Model of Building Considering Multi-Type Segmentation
## 1 Introduction
The second category is an indoor data model that focuses on the expression of spaces [17; 18; 19; 20; 21; 22; 23; 24; 25]. The space elements are used to represent the actual or theoretically bounded area or volume and provide certain specific functions in the building [26; 27]. Spaces are a generalization of all spatial elements that can be used to define a spatial structure or spatial region. In the IndoorGML standard, indoor space is defined as a space composed of architectural components in one or more buildings. IndoorGML defines four types of indoor spaces: General Space, Connection space, Anchor Space, and Transition Space. The current version of the standard is mainly for indoor navigation applications, focusing on expressing the semantic and topological information of indoor spaces, ignoring the space occupied by indoor furniture and other components [27]. The spatial element in the IFC standard is a generalization of all spatial objects that can be used to define an indoor spatial structure or area. IFC divides the spatial elements into three levels: building (IfcBuilding), floor (IfcBuildingStore), and space (IfcSpace) from large to small granularity. Among them, the smallest granular space (IfcSpace) represents the actual or theoretically bounded area or volume, which is directly related to the building floor. However, due to the placement of indoor furniture in the interior space, a smaller-grained functional space is formed, which is ignored by the current space model. Some researchers have developed several frameworks to further refine the indoor space granularity [19; 28; 29; 30; 31; 32; 33]. [PERSON] et al. (2014) propose to divide the indoor space into free sub-spaces and inert sub-spaces. [PERSON] et al. (2016, 2017) proposed a flexible space subdivision framework (FSS) for indoor navigation, which divides indoor space into object spaces, functional spaces, remaining free space, and agent spaces based on the ability of indoor objects to change their positions autonomously. The indoor space model that takes into account the space segmentation has an important influence on indoor navigation, indoor map service, and indoor space facility management. For example, fine navigation based on functional subspace division, payment management based on household space, etc. However, from the perspective of the semantic hierarchical classification of indoor space, the current standards and related research mostly focus on the three scales: building, floor, and room space. The semantic, relationship, and attribute information related to the household space and the functional subspace are missing from the current standard. Focusing on the expression of a single space, and lacking a description mechanism for multi-level complex indoor spaces under considering spatio-temporal segmentation. At the same time, without the subdivision of the room space by the furnishing components, most models lack the expression of the functional subspaces within a single space. Indoor furniture such as bookshelves will also subdivide the function of the indoor room, dividing a single room into two parts: the meeting function area and the office area. How can the existing IFC specifications be extended to include the expression of household space and functional subspace, so that it can support refined analysis and management applications, is the research question that this article needs to focus on. In view of the above-mentioned problems existing in the existing indoor space model, the contribution of this paper was to propose a multi-level unified expression indoor space data model considering the multi-type segmentation, which covers household space and indoor functional subspace. From the perspective of indoor space characteristics and cognition, with a focus on indoor space segmentation characteristics, the indoor space elements were analyzed in terms of semantic hierarchical structure, classification system, attributes, and relationships.