Accuracy and Project Cost Comparison Between Photogrammetry and LiDAR-based Methods in Sewer Manhole Inspection Data Capture and Condition Assessment
Bibliographic record
Abstract
Accuracy and Project Cost Comparison Between Photogrammetry and LiDAR-based Methods in Sewer Manhole Inspection Data Capture and Condition AssessmentAbstractSewer infrastructure management is on the verge of significant transformation driven by advancements in cloud computing, photogrammetry, and availability of high-resolution 360 'Action Cameras'. This presentation takes a critical look at this innovative approach, which uses data captured with consumer-available 360 'Action Cameras' to convert manhole inspection videos into intricate, textured 3D models of sewer systems. Notably, this session will feature a comprehensive objective comparison of field data accuracy, production/efficiency, limitations, and lessons learned. At the core of this approach lies photogrammetry, a sophisticated mathematical method that translates digital pixels in inspection videos into precise 3D points. Within the rendered 3D models, users can swiftly assess sewer components, perform custom measurements with centimeter-level accuracy, and can export this data into popular CAD software, for additional uses. The presentation explores how this new approach addresses longstanding challenges utilities have faced in sewer manhole assessment. Central to this discussion is a deep dive into the objective comparison of field data accuracy and field production and efficiency when contrasted with legacy manhole scanning systems. We will explore real-world scenarios and outcomes on projects in Houston, TX; Los Angeles, CA; Alexandria, VA; Toronto, ON; and other North American locations, shedding light on the practical implications of this technology. In addition, new cloud-based workflows for data capture, data transfer, stakeholder alert/review, and final submittal will also be described and compared to traditional methods of on-premises and physical data transfer. Furthermore, this presentation will scrutinize the limitations of this approach and draw a comprehensive comparison between photogrammetry and LiDAR, two prevalent methods in the industry. As we peer into the future of sewer infrastructure management, this session promises to provide valuable insights, backed by empirical data, and aims to foster an informed discussion about newly available options for sewer utilities, services contractors, and civil engineers. The presentation will conclude with summaries of known use-cases, as well as future use-cases and capabilities enabled by current technologies, such as Digital Twins and 3D modeling of horizontal sewer structures. Join us in this pivotal exploration of advancements that are shaping the future of sewer infrastructure management.This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.SpeakerMcGarry, TimPresentation time10:45:0011:15:00Session time08:30:0011:45:00SessionCollection System InspectionSession number28Session locationConnecticut Convention Center, Hartford, ConnecticutTopicCoastal Systems, Collection Systems, Condition Assessment, Consent Orders, Construction, Design considerations, Flow control, Force Mains, Infiltration/Inflow, Innovative Technology, LiDAR surveying, Pipe, Pipe Failures, Real Time Decision Support System, Real-Time Control, Rehabilitation, Slip line, Utility Management, Wastewater ManagementTopicCoastal Systems, Collection Systems, Condition Assessment, Consent Orders, Construction, Design considerations, Flow control, Force Mains, Infiltration/Inflow, Innovative Technology, LiDAR surveying, Pipe, Pipe Failures, Real Time Decision Support System, Real-Time Control, Rehabilitation, Slip line, Utility Management, Wastewater ManagementAuthor(s)Sullivan, EricAuthor(s)E. Sullivan1, T. McGarry1Author affiliation(s)SewerAI 1SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Apr 2024DOI10.2175/193864718825159365Volume / Issue Content sourceCollection Systems and Stormwater ConferenceCopyright2024Word count20
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".