# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 4 Taxonomy of High-Level Abstraction Category of Performed Process
### Taxonomy Discussion  
While preparing the second taxonomy, we focused more on the processing methods. We can draw a series of conclusions and observations thanks to the analyzed algorithms and the variety of results they generate. **Main methodology. ** There is a tendency to change the most popular and effective algorithm according to the processed category. Generally, analyzed solutions focused on two approaches--algorithmic processing or machine-learning methods. Pure segmentation, in which no weight was given to room-type recognition and without deeper semantic recognition of spaces, was performed mainly with repeatable algorithms. They process the readings just like any other multidimensional data, e.g., using clustering methods, variations of watershed algorithms, Generalized Voronoi Graphs, and many others. Repetitive processing of relatively simple input data yielded satisfactory results. On the other hand, solutions focused solely on classification are most effectively implemented by using complex input structures, large neural network models, and different machine-learning \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & \multicolumn{4}{c}{**High-Level Solution Category**} \\ \hline
**Data Type** & **Segmentation** & **Segmentation +** & **Segmentation + Precise** & **Precise Classification** & **Precise Classification** \\ \hline Floor Plan/Sketch & [86–88,90–97,103,108,113, 116,118,120–122,124,125] & [102, 106, 110, 110, 126] & [89, 98–101, 101, 104, 105, 107, 109, 111, 112, 114, 115, 117, 119, 123] & - \\ \hline Occupancy Map & [1, 139–132, 134, 135, 137–139, 142, 143, 145, 146, 148] & [127, 128, 133, 136, 140, 141, 143, 144, 147] & - & - \\ \hline Environment Picture & [151, 153, 154, 170] & - & [156] & [149, 150, 152, 155, 157–169, 171] \\ \hline
3D Spatial & [86–14, 16–21, 23, 25–32, 34–49, 51–55, 57–68, 70, 71, 74–77] & - & [22, 24, 33, 50, 56, 69, 78] & [15, 72, 73] \\ \hline Laser Range Measurement & [176, 177] & - & - & [175, 178, 179] \\ \hline Mixed & [181–185] & [189] & [180, 187, 190–195, 197, 198] & [186, 188, 196] \\ \hline Radio Signal Fingerprint & [200] & - & [199] & - \\ \hline Sound Echo, Chirp, RF & [204] & - & - & [201–203, 205] \\ \hline CAD-Like Data & - & [172] & - & - \\ \hline Graph & - & - & [79, 80, 82, 83] & [81, 84, 85] \\ \hline Energy & - & - & - & [173, 174] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Referenced papers grouped by the input data type and high-level solution category. approaches. Intermediate solutions combine both features and perform individual subtasks algorithmically or based on artificial intelligence and then combine the results. Other approaches, although present, were noticeably less popular or effective. **Semantic meaning. ** The more semantically rich the result needed to be, the more difficult it was to achieve it. Simple segmentation algorithms are successfully used in solutions where the collected information presents some abstract form of the world. An example can be the occupancy map that stores only three types of value in its pixels--empty, occupied, or unknown. Algorithmic methods perform well in such data, but it is a very limited representation of the real world, with no deeper semantic meaning that can be retrieved. When the actual space is represented with'real input', that is, photos taken by agents, video streams, or point clouds, and the task was to \"understand\", the simplest algorithms cease to work. The input of even the same type is too diverse, and there is no easy way to specify definite criteria for the classification. Even humans cannot always say what determines a space membership to a specific category. We subconsciously 'know' it, but it is not enough to prepare an algorithm. In such a situation, machine-learning solutions begin to dominate. Due to their nature and simultaneous processing of millions of parameters, they can detect rules and dependencies that are invisible even to humans. In general, the more semantic meaning we want to retrieve, the more real-life input needs to be processed.