As South Korea is expected to become a super-aged society by 2025, diligent efforts have been made to reduce the social burden by promoting a productive and healthy elderly lifestyle. Individuals are encouraged to prepare throughout their early lives and construct a healthy residential environment. As one of several alternatives home healthcare is using the Internet of Things—referred as ambient assisted living (AAL)—is drawing much attention. Although the individual technologies of healthcare and smart home have undergone rapid development, there has been little integration between the two. Moreover, many technological developments do not consider the actual lives of the elderly. To effectively respond to the aging problem, the two technologies should be integrated and applied to residential environments based on daily routines of the elderly. The purpose of this study is to suggest the future direction for healthcare in smart homes in South Korea. To achieve this goal, we first examined the possibility of embedding healthcare services into smart homes in a non-invasive manner. Second, in-depth interviews were conducted with elderly citizens and silver town operators to elucidate characteristics of the elderly, and a healthcare scenario was suggested that could be applied to each smart home room.

In the 21st century, humanity is facing unprecedented climate and food crises caused by population growth, urbanization, environmental pollution, and carbon emissions. As a response to the climate and food crisis, the following concept has emerged: smart urban agriculture that can reduce carbon emissions from buildings and achieve self-sufficiency in food. Various architectural designs that include smart farms are being explored worldwide. Nevertheless, the concept does not seem to have gained sufficient popular traction. This study attempted to materialize the concept by presenting types and characteristics from an architectural planning and design perspective by examining cases of smart farm constructions worldwide. After collecting 171 smart farm cases from around the world and building a database in terms of city, architecture, environment, and crops, the types were classified through SOM analysis, an artificial neural network-based cluster analysis methodology. As a result of the analysis, smart farm types were classified into seven types, and the characteristics of architectural planning and design were extracted for each type. It is meaningful that a specific form was presented so that planning and design can be easily accessed according to the situation placed through the type of smart farm.

Although the Korean government has provided high-quality architectural building information for a long period of time, its focus on administrative details over three-dimensional (3D) architectural mapping and data collection has hindered progress. This study presents a basic method for extracting exterior building information for the purpose of 3D mapping using deep learning and digital image processing. The method identifies and classifies objects by using the fast regional convolutional neural network model. The results show an accuracy of 93% in the detection of façade and 91% window detection; this could be further improved by more clearly defining the boundaries of windows and reducing data noise. The additional metadata provided by the proposed method could, in the future, be included in building information modeling databases to facilitate structural analyses or reconstruction efforts.

This study focus on the phenomenon of the preference for co-living among young adults that has manifested in South Korea. The study examines life in a shared house as a living place, which is the representative form of co-living that the younger adults in South Korea have been choosing. The objective of the study is to examine shared housing as living place matters and their possibility of being a home and house for the young generation. The study procedures included reviewing place attachment theory, analyzing the operational structure of shared houses, and interviewing residents to discuss the place attachment of the residential environment in shared houses. The young adult generation who chose to share a house display indecision on the issue of residential choices and behavior in terms of spatial possession. The results are as follows. Although co-living is a realistic residential choice for the reduction of residential costs, the majority of young adults experientially highlight the values of co-living rather than acknowledge the real reasons behind their choices. Such results signify that they recognize such limited residential choices as a means of temporary residence, not rooted to a living place, rather than an ordinal difference between the best and the second best, and ultimately the need to further consider the issues of continuous life and lifestyle on the foundation of the perspective of the universal life cycle of the young adult generation.

The Bukchon area in Seoul boasts a high density of Hanok, the traditional Korean architecture representing the region. Because the Hanok facade plays a vital role in the streetscape formation, we must record it in terms of social, cultural, historical, artistic, and scenic values. However, recording the facade of an existing Hanok building through drawing or image information is time consuming and labor intensive, and therefore costly. Further, its digital conversion is inherently difficult. This study proposes the use of deep learning to identify the form elements that comprise the Hanok facade. Three-dimensional modeling was performed on 405 well-preserved Hanok facades in the region, and 2808 items of image data were created under similar conditions and at differing angles. Labeling was performed on the shape elements of the Hanok facade, and a methodology was established to identify the facade elements using MASK R-CNN. The type of roof, windows, the lower part of the outer wall, and the design were identified with high accuracy.

This study applies an integrated analytical framework combining GeoDetector and multiscale geographically weighted regression (MGWR) to examine how the spatial distribution of cultural heritage values in the Chungcheong region of South Korea (Chungcheongnam-do and Chungcheongbuk-do) relates to regional socio-spatial contexts. Using the Korea Heritage Service’s heritage basic survey data (coordinates, attributes, and value assessments), we aggregated heritage value scores to a 1 km grid and modeled six value dimensions—historical, artistic, academic, social, rarity, and conservation—as separate dependent variables. We then integrated socio-spatial indicators derived from statistical grid maps published by the National Geographic Information Institute (official land price, building density, green space, road accessibility, total population, working-age population share, and aging rate). GeoDetector was first used to identify key determinants and interaction effects by value dimension, and MGWR was then used to estimate local effect heterogeneity and variable-specific operating scales. Results show that heritage values are better explained by multi-factor configurations—urbanization, land value, green space, accessibility, and demographic structure—whose importance varies by value dimension, and that the same factor can exert different directions and strengths across local contexts. By linking “what matters” (key determinants) with “where and at what scale it matters” (local effects and bandwidths), this study provides quantitative evidence to support place-based conservation and utilization strategies. The proposed GeoDetector–MGWR framework is transferable to other regions where spatial heritage inventories and comparable socio-spatial indicators are available.