# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
###### Abstract  
The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods. decade survey; indoor environment analysis; BIM; machine learning; room segmentation; room classification +
Footnote †: journal: Medical Informatics  
We can construct a digital twin of the building and create a 3D model of it. The floor plan can be reconstructed using drones flying through the factory. This is all possible but requires excessive time and (most likely human) resources. The problem becomes urgent when we consider the number of measurements and other data demanded by modern machine-learning-based solutions to work efficiently. To achieve the best performance possible, all the mentioned smart systems require a complete semantic understanding of their application field and its high-quality models. For example, a wearable tracking bracelet needs to know what room the calculated employee's coordinates represent. This creates a niche for systems capable of moving building data from any available source (like paper, pictures, or the existing property itself) to e-resources in an automated way. The best possible scenario would be to find a system that creates the documentation without user interaction other than data input. It should recreate not only the model but also understand its components and retrieve their meaning, most likely with extensive use of artificial intelligence and machine-learning-based decision models. One of the aspects of data processing in such systems is how to accomplish the operation of space segmentation (e.g., into rooms) and its categorization (e.g., into types of rooms). Although multiple potential solutions are available, they all noticeably differ in terms of used data structures and methodologies. In the literature, even a fast search for solutions based on a specific input data type (like a floor plan or point cloud) returns many possibilities and different approaches. They stretch from typical ones, such as the spatial data of 3D point clouds or colorful 2D images, to extraordinary ones, like sound chirps or energy measurements. The **primary motivation** for our work is this unprecedented diversity of possibilities. The industry needs to choose proper input data and select it based on the expected implementation reality, such as the limited computing power of sensors or the short battery life of robots. However, there is a visible lack of proper systematic reviews and surveys that would organize already proposed, implemented, and tested solutions with a particular focus on the processed data, utilized methods, and their actual applications. To the best of our knowledge, in the last decade, there has been only one survey discussing the topic of room segmentation on sample input data type of floor plan images--the one conducted by [PERSON] et al. [1].