Objective Constrained by operator proficiency and subjectivity, the traditional method for identifying high-consequence areas (HCAs) in gas pipelines exhibits shortcomings such as low efficiency and nonnegligible human errors. In recent years, the integration of satellite imagery data and Geographic Information System (GIS) technology has alleviated these deficiencies to some extent. Nevertheless, meeting the requirements for data diversity in identifying and grading HCAs in gas pipelines using only one data source remains a challenge. This constraint further hampers the quantification and automation capabilities of the identification and grading process. The study introduces an approach for identifying and grading HCAs in gas pipelines leveraging spatial and attribute information of buildings based on multi-source data.

Methods Based on high-resolution orthoimages captured by unmanned aerial vehicles (UAVs), a Squeeze-and-Excitation U network (SEU-Net) was developed as a deep learning model. This model improves the extraction of building edges to outline buildings around pipelines from the orthoimages. Points of interest (POI) were introduced to collect data on building types. Furthermore, a precise Digital Elevation Model (DEM) and Digital Surface Model (DSM) were created using 3D point clouds to assess building heights. The spatial analysis feature and attribute processing capabilities of GIS were utilized to calculate building areas and integrate spatial and attribute data. Moreover, an algorithm for identifying and grading HCAs in gas pipelines was devised.

Results The developed approach was validated in the scenario of a gas pipeline situated in Suining, Sichuan Province. In contrast to the outcomes achieved through the conventional identification technique, this method successfully detected 17 buildings that were missed in the manual process, resulting in a decrease of 0.369 km in incorrectly identified pipeline sections with HCAs.

Conclusion The methodology showcases greater accuracy and efficiency compared to the traditional method, highlighting its strong potential for practical application in the realm of gas pipeline integrity management.