New AI Tool Helps Cities Measure Building and Pavement Coverage

What is it about?

Our research introduces GraphParcelNet, a system that efficiently measures how much of each property is covered by buildings, driveways, and other hard surfaces that prevent water from infiltrating the ground. Traditional methods analyze high-resolution aerial photos, which is time-consuming. Our approach instead uses property boundaries and neighborhood demographics to predict these measurements, completing analysis for an entire city in seconds rather than hours.

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Photo by Eugene Triguba on Unsplash

Photo by Eugene Triguba on Unsplash

Why is it important?

Accurately measuring building and pavement coverage helps cities manage stormwater infrastructure and set fair utility fees. Cities previously relied on rough estimates by engineers or slow computer predictions from analyzing detailed aerial photos taking hours to process a single city. Our method completes the same analysis in just seconds while maintaining accuracy. This rapid assessment helps ensure property owners are charged fairly for stormwater services based on their actual amount of impervious surface, as property features such as driveways and buildings directly affect how much runoff enters the stormwater system. This efficiency enables cities to maintain more precise measurements of impervious surfaces across all properties, leading to more equitable fee structures and better stormwater management planning.

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