# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.9 Segmentation
In the detected rooms, their actual areas were calculated and used in a linear regression model that deducts the type of room. All other published works found used some sort of neural network. The architecture of Convolutional Neural Networks (CNNs) was widely found. In 2019, [PERSON] et al. [100] focused on the problem of the low availability of annotated floor plans that could be used by researchers. The presented dataset of images, called CubiCasa5k, made 5000 large-scale floor plans available for further analysis. While introducing the dataset, they also showed a CNN for a baseline results comparison. In the same year, [PERSON] and [PERSON] [118] presented research on the use of Mask R-CNN in the room feature segmentation. Although they were aware of CubiCasa5k's publication, it was too late to use it. The research was concluded based on 700 other annotated images, documenting the applicability of Mask R-CNN to the problem. Both articles were referenced by [PERSON] et al. in [109]. Their publication presented an improved approach based on a combination of Cascade R-CNN and Keypoint R-CNN. The final floor plan parser was reported to achieve better performance on the CubiCasa5k dataset than the initial author's baseline results. A specific variation of implemented tasks was presented by [PERSON] et al. [106]. In their solution, only bedrooms were recognized and counted in the analyzed floor plans. The described algorithm had extensive initial image processing (with Otsu thresholding, morphological operations, Hough transformation, and the application of the FAST algorithm), with the final bedroom detection achieved with a CNN. Five architectures were tested, and GoogLeNet was reported as the best-performing one. [PERSON] et al. [101] used the spatial context of elements found in the floor plan. The prepared multitask neural network used convolutional layers to carry out subtasks of room-type and room-boundary predictions. The later combination of results with spatial context awareness of structures hierarchy improved the achieved performance. [PERSON] et al. [123] tried to detect not only rectangular but also circular rooms. The presented Adversarial Network was constructed using direction-aware, additive convolutional kernels, which reported improving performance in irregular-shaped room detection. [PERSON] et al. [105] presented an architecture called MapSegNet--a variation of the typical encoder-decoder solution. Using the technique of skip-connections between different layers of the network, they reported comparable results with higher accuracy or lower computation costs compared to other architectures. [PERSON] et al. [108] focused their research on rural areas and presented a network processing the floor plans of 800 residences from the China region. As object recognition and text detection are typically two different tasks, their architecture primarily tried to reduce the time needed to train two different networks and combine them into one bigger system. [PERSON] and [PERSON] [126] made use of a Graph Neural Network. In their algorithm, an input image was first pre-processed (text removal and binarization), then vectorized, and finally represented as a Region Adjacency Graph (RAG). In such a graph, a GNN was used to find the semantic meaning of floor plan elements. Grid maps, as the second subtype of analyzed data types, were also the second-most numerous and similar to floor plans in the data processing aspect. We found an example of an evolving solution in the works of [PERSON] et al. First found in [128], from 2014, the work presented how to build a semantic map of the environment based on the concept of building typology. In the article, the authors assumed that each property type has a different typical indoor structure of rooms. Using such an assumption, they proposed a classifier of rooms specific to a known building type. In the later article [82] from 2019, they tried to predict the layout of rooms only from a partially observed grid map.