Domain knowledge integration method for landslide disaster risk assessment by a large language model and image segmentation

Abstract

The utilization of "collaborative AI robot groups" has potential in rapid emergency recovery operations as disasters have recently become more frequent and severe. River channel blockages may cause secondary disasters, which unmanned construction machines need to autonomously avoid. However, that is challenging due to the insufficient availability of AI training data in disaster scenarios. To address this issue, this study provides a workflow for assessing the dangers by integrating AI with human knowledge and experience, which have been accumulated in natural language. First, the analysis results of a landslide site were obtained by using Semantic Segmentation. Secondly, they were transformed into fixed phrases and fed into a large language model (LLM) that had been fine-tuned for landslide disasters to determine the risk. This research exhibits an application example that combines image AI technology, knowledge of civil engineering, and the rapidly developing LLM.