2025 Volume 6 Issue 3 Pages 348-358
This paper presents a land-use classification model based on deep learning-driven semantic segmentation using aerial photographs provided by the Geospatial Information Authority of Japan. The proposed method aims to automatically generate high-resolution land-use data (approximately 30 cm) compared to conventional 100-meter land-use mesh datasets, thereby enhancing the applicability of land-use information in flood simulations and urban development. To improve classification accuracy, a PSPNet architecture augmented with an attention mechanism was employed to address class imbalance, achieving superior performance over traditional methods. Additionally, we evaluated the generalization performance of the model across two regions with unseen data to verify its adaptability to different regional characteristics. The proposed model was further applied to tsunami simulations, demonstrating a significant improvement in processing efficiency compared to manual interpretation methods. The results showed that the predicted shoreline positions in tsunami run-up analyses achieved accuracy comparable to ground truth labels.
