An intelligent identification method for high consequence areas of natural gas pipelines integrating multiple attention mechanisms

As the demand for the safe operation of oil and gas pipelines grows, precise identification of high consequence areas has become crucial for pipeline safety. Traditional manual methods are highly subjective, prone to errors, and unsuitable for large-scale pipeline assessments. While existing intelligent methods offer automation, they depend on the integration of multiple heterogeneous software platforms, leading to complex data interactions and challenges in achieving fully automated, end-to-end processes. To this end, this study proposes an intelligent identification method for high consequence areas of natural gas pipelines integrating multiple attention mechanisms was proposed. A high consequence area identification database containing POI venue category data, UAV orthophotos, and three-dimensional reconstruction results was constructed, and the U-Net model integrating multiple attention mechanisms was employed to achieve precise identification of building outlines within high consequence areas. The experimental results indicated that the identification accuracy of this method reached 97.54%, 97.78%, and 94.44% in urban, urban-rural fringe, and rural settings, respectively. By integrating geographical coordinates, special venue attributes, and three-dimensional height information from the database, precise classification of the regional and high consequence area levels for a specific area in Shanxi was achieved, offering a scientific foundation for the safe operation of natural gas pipelines and the intelligent development of pipeline integrity management.