PARTNERSHIPS

Rural Water Systems Get AI Help Tracking Leaks

State-backed LeakTracer uses satellite AI tools to help rural utilities find hidden leaks faster

10 Mar 2026

Field technician using handheld device for water system inspection

New Mexico is expanding the use of artificial intelligence in its water infrastructure through a programme designed to help rural utilities detect hidden leaks more quickly.

The initiative, called LeakTracer, was announced by the New Mexico Environment Department on January 23. It gives smaller drinking water systems access to satellite-based detection tools that many local utilities would struggle to finance on their own.

The programme works with technology provider ASTERRA and engineering firm McKim & Creed. Together they analyse L-band synthetic aperture radar data gathered by satellites, alongside AI-generated assessments that identify areas where underground leaks are likely.

State officials say the approach can shorten the time required for field crews to confirm and repair leaks in ageing distribution systems.

Early results from pilot deployments suggest the technology could significantly reduce water losses. In five participating communities, the programme identified more than 78 verified leaks and helped save an estimated 345,000 gallons of water per day during the first half of 2025.

Water loss is a persistent issue in many parts of the state. Officials estimate that some systems lose between 40 and 70 per cent of treated drinking water before it reaches customers, often because of ageing pipes and delayed detection of underground failures.

LeakTracer is aimed at utilities serving fewer than 20,000 residents that are in good standing with New Mexico’s Rural Infrastructure Loan Program. Leak detection services are covered by the state initiative, while participating utilities remain responsible for funding repairs once leaks are confirmed.

The programme does not address the broader financial and labour constraints facing rural utilities. But state officials say it reflects a wider shift in which AI-based monitoring tools are moving from limited pilot projects into regular public infrastructure operations.

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