Buried Utility Pipeline Mapping Based on Multiple Spatial Data Sources: A Bayesian Data Fusion Approach
Huanhuan Chen, Anthony G. Cohn
Statutory records of underground utility apparatus (such as pipes andcables) are notoriously inaccurate, so street surveys are usually undertakenbefore road excavation takes place to minimize the extent and duration ofexcavation and for health and safety reasons. This involves the use ofsensors such as Ground Penetrating Radar (GPR). The GPR scans are thenmanually interpreted and combined with the expectations from the utilityrecords and other data such as surveyed manholes. The task is complex owingto the difficulty in interpreting the sensor data, and the spatialcomplexity and extent of under street assets. We explore the application ofAI techniques, in particular Bayesian data fusion (BDF), to automaticallygenerate maps of buried apparatus. Hypotheses about the spatial location anddirection of buried assets are extracted by identifying hyperbolae in theGPR scans. The spatial location of surveyed manholes provides further inputto the algorithm, as well as the prior expectations from the statutoryrecords. These three data sources are used to produce the most probable mapof the buried assets. Experimental results on real and simulated data setsare presented.