An Underground Pipeline Mapping Method Based on Fusion of Multisource Data
Xiren Zhou, Qiuju Chen, Bingbing Jiang, Huanhuan Chen
Abstract
There is a need to map underground pipelines due to non-available existing pipeline maps caused by poor management of statutory records and insufficient updating of documentation whenever pipeline construction or rerouting occurs. By fusing multi-source data, a novel method to map underground pipelines is proposed in this paper. Statutory records of the underground pipelines are converted to the initial pipeline map. Pipeline information obtained from manhole covers and remote sensing technologies are normalized into the pipeline data set composed of detected points. The Probabilistic Pipeline Mapping Model (PPMM) is then proposed to map the buried pipelines from the conducted pipeline data set, with or without statutory pipeline records. In this model, each detected point is classified into the specific pipeline that most likely generates the data of this point, and detected points generated from the same pipeline are fitted to revise the pipelines’ locations and directions. The above classification and fitting operations are performed iteratively, and PPMM would output the pipeline map with the highest probability. Experimental studies on real-world datasets are conducted and analyzed, and the obtained results demonstrate the effectiveness of the proposed method.