Classification of Subdivision Land Use and Land Cover Using Deep Learning Models

Land cover provides crucial information related to biological geography, ecological climatology, and human activities. In the past, land cover mapping was performed based on visual interpretation, but it had limitations in terms of time and cost. Recently, it has become possible to create land cover maps with higher temporal resolution over wider areas using artificial intelligence-based models. The accuracy and reliability of AI model-based land cover maps increase with the amount of training data, but it is difficult to acquire large amounts of data due to the time required for label data annotation. In South Korea, the Environmental Geographic Information Service provides self-learning data consisting of aerial orthoimages and subdivision land cover classification level label data, making it possible to collect high-quality data. Therefore, this study examined the feasibility of self-learning data by building and evaluating a U-Net-based land cover classification model for waterfront areas using self-learning data. The trained model showed relatively low performance with an F-1 score of 0.61 for training data and 0.31 for test data. The model’s low performance is thought to be due to insufficient training caused by the large number of classification categories (34) and data imbalance between categories. Although the model performance using self-learning data was low, it is believed that model performance can be improved by grouping classification categories according to research purposes or resolving data imbalance through data augmentation techniques. Therefore, self-learning data is expected to be utilized in various studies using land cover.