# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 8 Newest Trends and Place for Future Work  
The extensiveness of the conducted research forced a specific range of publication dates to be accepted. As already presented, indoor environment analysis is a dynamically evolving field. Nevertheless, later in this section we try to indicate the main future directions of research.  
One of the most important, inevitable trends is the popularization of IoT sensors and their use in everyday life. They can be integrated into various home appliances. Inspired by neuroscience, [PERSON] et al. [223] presented scene classification in the application of home service robots. [PERSON] et al. [224] benefited from the diversity of available data representations and used a set of numerous formats of maps to create an autonomous navigation and landing system for an Unmanned Aerial Vehicle (UAV). Finally, [PERSON] et al. [225] reviewed the role of IoT sensors in the aspect of fire hazard contingency in smart homes. **Internet-connected devices have become a standard in the industry, and we should place special attention on the utilization of the possibilities it generates.**  
Another important trend is the increasing capability of large-volume data processing with gradually more sophisticated methods. [PERSON] et al. [226] proposed a well-performing framework for scan-to-BIM generation with semantic segmentation of input point clouds. They report values for the precision, recall, and F-score of indoor element reconstruction of 96-99%. A full-scale digital twin was proposed to be automatically generated and used in a later production system's simulation by [PERSON] et al. [227]. They processed huge amounts of data to plan and predict the manufacturing process, optimize it, and provide cost savings to clients. Images with an additional channel of depth were analyzed by [PERSON] et al. [228] with the use of transformer neural network architecture, which is gaining popularity. **All these methods were possible to implement thanks to the increasing computing power of typical workstations.**  
Another aspect we would like to mention is the carbon emissions reduction and energy optimization of multiple types. This can be done at a micro- as well as a macro-scale. Single building modeling and its property analysis were analyzed regarding various aspects. [PERSON] et al. [229] presented the context of the thermal storage performance of a building; [PERSON] et al. [230] studied the self-consumption optimization of energy produced by pho tovoltaic systems; [PERSON] et al. [231], [PERSON] et al. [232], and [PERSON] and [PERSON] [233] focused on the personal satisfaction (e.g., the thermal comfort) in connection with the building energy consumption. At the macro-scale, industrial analysis of energy quota trading was presented by [PERSON] et al. [234]. **Society's ecological awareness is growing, leading to an increase in the number of analyses focusing on energy optimization.**  
We left the most important trend for last: **the application of machine-learning in the analysis of indoor environment has become a standard approach**. It can be clearly seen with a search for the set of keywords'machine-learning indoor environment' in the online repositories of MDPI, Elsevier's Science Direct, and IEEE Xplore. The summarized number of records found in all these three repositories reached 2265 for the year 2021; in 2022, it was already 2878 (+27% year-to-year), and in 2023 the number of entries increased by a further 20% and reached 3454 records.  
All these trends are well-suited to be the direction of future research, and their results will definitely attract a significant amount of readers' attention.