## 3 Results
### Keywords  
The following section analyzes the keywords of the literature database according to the co-occurrence network. The analysis of keywords represents the core content of published documents and cover the range of areas researched within the boundaries of any domain. The analysis was performed using the all keywords unit of the downloaded data to create a map by selecting a threshold with a minimum of five co-occurrences of a keyword, which led to the 87 most frequent keywords in the database (initially, 2346 keywords were identified). In the analysis, shown in Figure 7, each keyword is represented by a node whose size depends on the frequency of occurrence. The colours of the keywords represent clusters of terms that most often appear together. The automatic clustering process is useful to get an overview of the assignment of keywords to clusters and how the keyword clusters are related to each other. It is important to highlight that the clustering process does not cover keywords that are synonyms, only keywords that appear together in the articles. For this reason, it is logical that the different words used to refer to an unmanned aerial vehicle do not appear in the same cluster, as it is confusing for the reader to use all synonyms in the same research without defining the light differences between them previously. Table 5 shows the top keywords most frequent per cluster, with more information from the analysis, such as the average year of publication, the link strength and the total link strength. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Keyword** & **Cluster** & \
\begin{tabular}{c} **N* of** \\ **Co-Occurrences** \\ \end{tabular} & **Average Pub. Year** & **Link Strength** &
\begin{tabular}{c} **Total Link** \\ **Strength** \\ \end{tabular} \\ \hline UAV & & 137 & 2017 & 85 & 641 \\ Photogrammetry & & 40 & 2017 & 59 & 201 \\ Buildings & & 30 & 2017 & 64 & 173 \\ Surveys & GREEN & 24 & 2018 & 67 & 159 \\ Data acquisition & & 21 & 2017 & 60 & 129 \\ Point cloud & & 16 & 2017 & 49 & 98 \\ LiDAR & & 15 & 2017 & 55 & 105 \\ Laser scanning & & 15 & 2016 & 54 & 86 \\ \hline Construction & & 16 & 2017 & 34 & 74 \\ Architectural design & & 13 & 2017 & 36 & 74 \\ Automation & & 11 & 2018 & 37 & 47 \\ UAS & RED & 10 & 2018 & 41 & 64 \\ Building Information & & 10 & 2016 & 32 & 63 \\ Model & & 9 & 2018 & 32 & 48 \\ Monitoring & & 9 & 2017 & 27 & 37 \\ Robotics & & 7 & 2017 & 24 & 39 \\ Accident prevention & & 10 & 2018 & 32 & 63 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of network analysis. The most frequent keywords per cluster. Figure 7: Keywords co-occurrence network analysis. Green colour: mapping and 3D modeling cluster, red colour: construction monitoring cluster, blue colour: structural damage detection cluster, yellow colour: energy efficiency prospection cluster, and orange colour: urban remote sensing cluster. According to the number of co-occurrences, the most frequently keywords are: UAV (137 co-occurrences), Drones (95), Photogrammetry (40), Aircraft (38), Buildings (30), Remote sensing (27), Surveys (24), Data acquisition (21), and Thermography (imaging) (19) (Table 5). They are grouped into five clusters which can be identified by the different colours. The links in Figure 7 show co-occurrence relationships between all of them. The spatial arrangement of the clusters seems circular, they are equally distributed and there are no large distances between them that would reveal gaps in the literature. The blue and red clusters are the smallest graphically as indicated by the total link strength values of their nodes. As expected, the most frequent keywords with the highest link strength and total link strength are those related to the main purpose of this research: UAV (85 and 641), Drones (82 and 488), Aircraft (64 and 201) and Buildings (64 and 173). The term UAS is less common, and RPAS does not seem to be used in the documents studied. The top keyword related to urbanism is Urban area with 14 co-occurrences.