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.