A Study on an Evaluation of the Managed Residential Environment Improvement Project Using Deep-Learning Model

Abstract

The managed residential environment improvement project was used as an alternative to renewal projects such as redevelopment and reconstruction to make the residential environment and streetscape better guaranteeing the residential rights of residents. However, after completing the project, it was not easy to sense the actual environmental changes owing to the lack of physical changes, and the evaluation was limited because of the difficulty of objectively proving the improvement through the project. In this study, a deep learning model was used to assess the streetscape of the 19 target sites before and after the project was implemented. Further, the differences were analyzed to check whether the streetscape had improved objectively by the project. The results were as follows. First, the changes in the streetscape as a result of the project were visible through the obvious changes in the four indicators. Second, the changes in the four indicators enabled to quantify whether the streetscape was improved or not. Third, a definite indicator of changes existed between the street on which the project was implemented and that on which it was not implemented. Based on these results, the actual impact of the managed residential environment improvement project on the streetscape and the environment was determined. This is expected to contribute to future projects in terms of direction, efficiency, and objective post-evaluation.