Geospatial AI for Water Network Operators

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Our Water Infrastructure Network Monitoring Service

Rezatec’s Geospatial Artificial Intelligence (AI) solution for water network operators combines remote sensing and data science to identify the conditions under which network failures occur and to quantify the Likelihood of Failure across the network. By identifying which sections of the network have a higher likelihood of failure, network operators can concentrate their ground survey teams and IoT sensors on those high-risk areas.

This highly targeted approach allows the optimisation of their repair and monitoring work therefore reducing cost and time to find leaks. Additional geospatial datasets can also be incorporated into the analysis to further mitigate high consequence risks and to digitize general knowledge and understanding of the network.

Actionable Intelligence

Better network planning and investment.

Data Driven Efficiency

Prioritize areas where Likelihood of Failure (LoF) highest.

Resource Optimisation

Prioritise your ground survey and IoT sensor deployment.

Improved Network Management

Reduced cost & faster time to find leaks.

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Rezatec Geospatial AI

Rezatec uniquely combines remote sensing analysis with data science to deliver Geospatial AI. Using machine learning driven statistical processing of satellite imagery and selected data inputs, Rezatec provides proactive risk management as a data service for your whole network. With subscription-based online access to our geospatial portal, our customers use our expertise and data driven analysis to enable reduced operating costs and optimised asset management across their networks. Rezatec customers cover the globe and are leaders in their respective industries in the water, agriculture, infrastructure and forestry sectors.

The Business Challenge

Faced with increasing challenges of reducing leakage and non-revenue water loss, waste and water network operators are looking for innovative methods to plan and optimize strategic investment in their pipelines.

Operationally, this translates as how to optimize the deployment of resources, repair and maintenance and capital expenditure based on the Likelihood of Failure (LoF) across the network.

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