Papers
1
Total Citations
8
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About
David Marlow is a leading researcher in infrastructure asset management, with a particular focus on the automated inspection and condition assessment of buried pipelines. His work bridges civil engineering and computer vision, developing intelligent systems that can interpret pipe inspection imagery—such as that captured by mobile robotic platforms—to detect defects and assess structural integrity without exhaustive manual review. Marlow’s contributions are foundational to automating what has traditionally been a labor-intensive, subjective process, enabling faster, more consistent, and more cost-effective infrastructure monitoring. His highly cited 2009 paper, “An Approach to Pipe Image Interpretation Based Condition Assessment for Automatic Pipe Inspection,” has garnered 8 citations and remains a key reference for researchers and practitioners seeking to integrate image analysis into asset management workflows. By advancing the automation of pipe inspection, Marlow’s work directly supports the proactive maintenance of critical underground water and wastewater networks, helping utilities extend asset life and reduce failure risks. His research continues to influence the development of smarter, data-driven approaches to urban infrastructure resilience.
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