Case Studies: Mainline TV Inspection and Location in Municipal Sewer & Water
Municipal Sewer & Water magazine features SewerAI's AutoCode technology in its March 2021 case studies issue, highlighting how AI automatically recognizes pipe conditions in CCTV sewer data to generate condition assessment reports.
SewerAI is proud to be featured in the March 2021 issue of Municipal Sewer & Water magazine, one of the leading trade publications serving the municipal water and wastewater industry. In an article authored by Craig Mandli, SewerAI's AutoCode technology is highlighted as part of the magazine's case studies series on mainline TV inspection and location — a testament to the growing role of artificial intelligence in modernizing infrastructure assessment.
Featured in Municipal Sewer & Water Magazine
Municipal Sewer & Water (MSW) is a trusted resource for engineers, operators, and decision-makers in the municipal utility sector. Being included in its case studies issue underscores the real-world impact that SewerAI's technology is having on how cities and utilities manage their underground infrastructure. The March 2021 issue focuses on mainline TV inspection and location — a critical workflow for any municipality responsible for maintaining aging sewer networks.
For municipalities, the ability to accurately assess the condition of sewer mains is essential for prioritizing repairs, managing budgets, and preventing costly failures. Traditional inspection workflows rely heavily on manual review of CCTV footage — a time-intensive process that can introduce inconsistencies and slow down reporting. SewerAI was built to address exactly these challenges.
How AutoCode Works
At the heart of SewerAI's platform is AutoCode — an AI-powered technology that automatically recognizes pipe conditions in CCTV sewer inspection data. Here's how the process works:
- CCTV footage from mainline sewer inspections is ingested into the SewerAI platform.
- AutoCode analyzes each frame of the video, automatically detecting and identifying pipe conditions such as cracks, root intrusions, joint defects, and more.
- When a condition is predicted, AutoCode brackets the relevant portion of the image and assigns a detailed label — logging both the type of condition and its distance within the pipe.
- The AI-generated output is then reviewed by an industry-certified analyst, who validates the findings and produces a final condition assessment report.
This human-in-the-loop approach combines the speed and consistency of machine learning with the expertise and accountability of certified professionals — delivering reports that are both faster and more reliable than traditional manual coding alone.
The Value of AI-Assisted Condition Assessment for Municipal Utilities
For municipal utilities managing hundreds or thousands of miles of sewer infrastructure, the stakes of accurate condition assessment are high. Missed defects can lead to sinkholes, sewage overflows, and expensive emergency repairs. Delayed reporting can stall capital improvement planning. AutoCode addresses these pain points in several meaningful ways:
- Speed: AI processes CCTV footage far faster than manual review, dramatically reducing the time from inspection to report delivery.
- Consistency: Automated detection applies the same criteria to every frame, reducing variability that can occur with manual coding across different analysts or shifts.
- Accuracy: The combination of AI detection and certified analyst review creates a quality-controlled workflow that improves overall report accuracy.
- Scalability: Utilities can process larger volumes of inspection data without proportionally increasing staffing costs, enabling more comprehensive asset management programs.
- Standardized Reporting: AutoCode outputs align with industry-standard coding systems, making it easy to integrate results into existing asset management and GIS platforms.
As municipalities face increasing pressure to do more with limited budgets, AI-assisted inspection workflows represent a practical and proven path forward. SewerAI's inclusion in Municipal Sewer & Water's case studies issue reflects the industry's recognition that technologies like AutoCode are not just innovative — they are becoming essential tools for modern utility management.
Read the Full Article
The full case study, authored by Craig Mandli, is available in the March 2021 issue of Municipal Sewer & Water magazine. Read the article online at mswmag.com.
