Large language model-driven knowledge graph reasoning for enhanced semantic segmentation.

ABSTRACT: Urban scene segmentation is essential for 3D city modeling and plays a crucial role in various remote sensing applications, including urban planning and environmental monitoring. While integrating knowledge graphs with scene segmentation has improved accuracy, existing methods often depend on dataset-specific knowledge graphs, limiting their generalizability across diverse remote sensing data. To address this, we propose a novel framework that leverages large language models (LLMs) to construct a universal knowledge graph from multi-source geospatial data and incorporate it into remote sensing semantic segmentation tasks, enhancing adaptability and robustness in urban scene understanding. Specifically, the framework comprises two key components: (1) a Graph Construction module that employs

SUBMITTER: Su J 

PROVIDER: S-EPMC12800038 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature