# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 2 Review Methodology and Conduction
### Filtering Criteria
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Title** & **Description** \\ \hline EC1 & Published before 2012 & To keep the research up to date, the survey conducted was focused on the newest methodologies—from the last decade only. \\ \hline EC2 & Duplicated article & As we searched multiple publication databases, the same article could be found in many different sources but was supposed to be analyzed only once. \\ \hline EC3 & Not written in English & English was chosen as the only accepted language. It was important to check the whole paper, as it happened to find results with English titles and abstracts but foreign language content. \\ \hline EC4 & Not concerning a topic, at least potentially related to the room segmentation or classification & Although we used a precise search query, the found papers’ relevance was not guaranteed. We checked them manually and verified if the general topic of the article discussed floor plan analysis, spatial data processing methods, or at least an issue that could lead to room segmentation in any different type of data. \\ \hline EC5 & Full text not found & Reliable paper analysis requires the publications to be read and understood. Titles or abstracts alone were not enough. \\ \hline EC6 & Does not describe the process in detail & Papers without a precise description of the methodology used were rejected. The presentation of only the research results was not enough to fully answer the research questions. \\ \hline EC7 & Describes only ideas, discussions, or interviews & The objective of this study was to include publications of substantial value and precise descriptions of the papers. They were required to be implemented reliably, tested, and their results had to be available. \\ \hline IC1 & The topic must indicate the, at least potential, use in the indoor environment & This paper focuses on closed spaces, which can segment rooms inside of a building, not areas outside of it. This criterion filtered out solutions dedicated to large-scale outdoor applications, like the analysis of aerial photos. \\ \hline IC2 & Method must include some form of automated processing & The idea is to compare systems of somehow unsupervised data processing. Descriptions of fully manual processes, design guidelines, or manually carried out reports were omitted. \\ \hline IC3 & Article must reference at least 10 other papers & As the survey should be based only on reliable and scientifically important articles, analyzed papers were expected to be based on at least ten reviewed references. \\ \hline IC4 & Solution must process room—or higher structure—level data & We want to filter out solutions focused on internal single-room analysis. An example of such a scenario was the furniture segmentation task or wall decor recognition. To fulfill this criterion, the algorithm had to be able to segment at least one instance of a room or one class for the whole room needed to be recognized. \\ \hline IC5 & Article must describe the achieved performance and datasets used & Only papers with reliable results presentations were accepted. To fulfill this criterion, a description of the performance evaluation method had to be presented. The public availability of the datasets was not required, but their description was. \\ \hline \end{tabular}
\end{table} 
Table 1: Filtering criteria used during the research, split into Exclusion Criteria (EC) and Inclusion Criteria (IC).