Landscape classification with self-organizing map using user participation and environmental data: the case of the Seoul Metropolitan Area

Abstract This study aimed to develop a method for assessing landscapes using environmental data and user-generated data, which are commonly employed in landscape research. It focused on the Seoul metropolitan area in South Korea, devising evaluation indicators for five key concepts: naturalness, diversity, imageability, historicity, and disturbance. These indicators were used to assess the landscapes based on each index. We employed a self-organizing map, an artificial neural network technique, to categorize the landscape units and developed eight evaluation indicators for the five key concepts, organizing the study area’s landscapes into six distinct landscape units. This study identified landscape unit types with increased vulnerability to visual blight or heightened public awareness by considering both user characteristics and environmental attributes in the metropolitan area landscapes. Finally, we discussed future tasks for appropriate landscape management based on each landscape area’s characteristics to maintain and enhance landscape quality.