Inferring the most probable maps of underground utilities using Bayesian mapping model
Inferring the most probable maps of underground utilities using Bayesian mapping model
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DOI：
10.1016/j.jappgeo.2018.01.006
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发表时间：
2018-03-01
影响因子：
2
通讯作者：
Cohn, Anthony
中科院分区：
文献类型：
--
作者：
Bilal, Muhammad;Khan, Wasiq;Cohn, Anthony
英国的一项重大举措-地下测绘（MTU），侧重于通过开发一种多传感器移动终端，解决由于无法定位埋在地下的公用设施（如管道和电缆）而造成的社会、环境和经济后果。MTU装置的目的是利用自动数据处理技术和法定记录，真实的实时定位不同类型的地下资产。法定记录，即使通常是不准确和不完整的，提供了有用的信息，什么是埋在地下和在哪里。然而，将来自多个传感器的信息（原始数据）与这些定性地图及其可视化相结合是一项挑战，需要实施强大的机器学习/数据融合方法。本文提出了一种基于贝叶斯映射模型的地图自动生成方法，该方法综合了传感器原始数据和现有法定记录中提取的知识。法定记录与传感器的假设相结合，用于初步估计地下可能发现的东西和大致位置。使用自动图像分割技术的假设提取和贝叶斯分类技术的部分人孔连接的地图（重新）构建。该模型包括图像分割算法和各种贝叶斯分类技术（段识别和期望最大化（EM）算法）提供了强大的性能在各种模拟以及真实的网站在预测线性/非线性段和构建精细的2D/3D地图。(C)2018作者由Elsevier B. V.发布，这是CC BY许可下的开放获取文章。
Mapping the Underworld (MTU), a major initiative in the UK, is focused on addressing social, environmental and economic consequences raised from the inability to locate buried underground utilities (such as pipes and cables) by developing a multi-sensor mobile device. The aim of MTU device is to locate different types of buried assets in real time with the use of automated data processing techniques and statutory records. The statutory records, even though typically being inaccurate and incomplete, provide useful prior information on what is buried under the ground and where. However, the integration of information from multiple sensors (raw data) with these qualitative maps and their visualization is challenging and requires the implementation of robust machine learning/data fusion approaches. An approach for automated creation of revised maps was developed as a Bayesian Mapping model in this paper by integrating the knowledge extracted from sensors raw data and available statutory records. The combination of statutory records with the hypotheses from sensors was for initial estimation of what might be found underground and roughly where. The maps were (re)constructed using automated image segmentation techniques for hypotheses extraction and Bayesian classification techniques for segment manhole connections. The model consisting of image segmentation algorithm and various Bayesian classification techniques (segment recognition and expectation maximization (EM) algorithm) provided robust performance on various simulated as well as real sites in terms of predicting linear/non-linear segments and constructing refined 2D/3D maps. (C) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.