# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Summary and Discussion
**Graph Generation** was the task implemented to utilize the advantages of graph structures, i.e., well-established analysis of relationships between the nodes. Here, we have a similar observation to the one from floor plan prediction--the creation of the graph takes place mainly based on input data with a higher degree of complication. The created graph is easier to process and better highlights simple dependencies, hard to notice in the data noise of more complex inputs. This task is not yet dominated by machine-learning solutions but with the increasing importance of the fairly novel idea of Graph Neural Networks and their variations. **Room Classification** is a complex task carried out for every one of the found data types. Subjectively, the best results are achieved while processing authentic images of the environment, taken manually by a human or from a mobile platform/droid. The deep neural networks and their convolutional subtype remain the most popular and unbeatable in the achieved quality of results. The most promising solutions are those analyzing three-dimensional point clouds. However, their degree of maturity is too low to consider them as the default choice. They have great potential but are subject to an extensive calculation overhead, which is not yet justified by the quality of the results. **Change Detection** was found to be a highly underestimated application that could noticeably improve the process of building lifecycle monitoring--only a single work focused on indoor environments. Most of the articles on this topic were filtered out of this research because the analysis focused on processing aerial photos. The use of building models to detect, analyze, and document changes remains a field for further investigation. The **Segmentation** task best shows the evolution of the input types and processing methods over time. Historically, the first was the segmentation of building plans. Their acquisition was the simplest and most widespread one. With time and the development of the available equipment (e.g., automatic cleaning robots), the segmentation issue expanded to occupancy maps. Finally, the most modern structure is the point cloud. Segmentation based on two-dimensional data is noticeably more explored than spatial data processing. Equipment capable of acquiring 3D representations of space is still a novelty that makes spatial data processing a leading topic of ongoing research in this field. It is worth noting that selected techniques that work well in two-dimensional data can be successfully adapted and applied to three-dimensional structures. This applies to transforming symbolic representations (occupancy maps) and real images. Computer vision techniques, for example, morphological operations, prepared originally to work with a matrix of pixels, can be effectively transferred to three dimensions. This requires some additional work and adjustments (such as the voxelization of models), but it is possible and has been successfully utilized. The same can be said about clustering, distance transform, watershed algorithms, etc. They worked well in the past and seem to work correctly nowadays. The only challenge remaining is to preprocess the data to present analogous structures properly but with an increased number of input dimensions. **Indoor Navigation** was the task that combined the previously described methods to achieve a new output and give it a more implementation-oriented application. Regardless of the specific subtask, the elements of segmentation and classification processes were carried out the same way as in the already discussed examples and then combined to obtain a more complex result. An issue more broadly and more thoroughly studied than Simultaneous Localization and Mapping (SLAM) was not described directly in this article. The **Alignment/Matching** task is performed mainly as a step in solving more complicated problems, such as navigation in the environment. It implements segmentation and classification as a means to reach a different goal.