Volume 68

An Intelligent Building Recognition and Grading Method for Pipeline High Consequence Area Identification Huanyu Zhao,Haochong Li,Rui Qiu,Yongtu Liang

Abstract

Accurate identification of high consequence areas (HCAs) is critical for oil and gas pipeline safety management, because the final grading depends on whether building exposure evidence within the pipeline buffer zone remains reliable from image interpretation to engineering rule evaluation. This paper presents a complete workflow that converts building recognition results from unmanned aerial vehicle (UAV) corridor imagery into structured engineering evidence for rule-based HCA grading. Building masks predicted by a deep segmentation model are restored to geographic coordinates, vectorized into footprint polygons, and intersected with the pipeline buffer. Each footprint is then combined with digital surface model (DSM) data to estimate building height, floor count, household number, and population. Point-of-interest (POI) records are spatially filtered and classified into hazardous-facility and sensitive-place triggers through keyword matching. The fused evidence vector, including building count, estimated households, population density, and POI triggers, is evaluated by a configurable rule operator that outputs both an HCA grade and its decision reason. Two decision-oriented reliability measures, evidence margin and grade-stability index, are further introduced to quantify how close a segment is to a rule boundary and whether plausible evidence perturbations would change the final grade. The proposed method provides a traceable pathway from UAV imagery to auditable HCA grading, supporting differentiated inspection planning in pipeline integrity management.

Keywords high consequence area,UAV remote sensing,building extraction,evidence fusion,pipeline integrity management