Identification Method of High Consequence Area of Pipeline based on Deep Learning and GIS Technology

High Consequence Areas (HCAs) represent geographical zones where pipeline leaks pose a significant threat to public safety and could result in severe environmental harm.The boundaries and locations of these HCAs are not static; they evolve as population dynamics and resource environments undergo changes.To address the intricate challenges of identifying and managing HCAs, a sophisticated method was devised that integrates deep learning and Geographic Information System (GIS) technology.This method harnessed a cutting-edge deep learning framework to recognize and analyze satellite imagery maps, coupled with advanced GIS buffer zone analysis techniques.The process encompassed a meticulous sequence of steps: data collection and processing, building extraction, regional classification, and HCA analysis specifically tailored for HCA identification.By resolving issues such as the burden of extensive manual data collection, inaccuracies in identification, and lengthy update cycles, this method achieved automated HCA identification, thereby significantly enhancing accuracy and consistency.The insights garnered from this study promise substantial alleviation of the manual data collection workload during the HCA identification process, while also bolstering the precision and uniformity of identification.This study offers invaluable insights and guidance for engineering practices pertaining to pipeline management.