Chesapeake Conservancy's Susan Minnemeyer and Dr. Kumar Mainali. (Courtesy photos)

Chesapeake Conservancy is datafying the country’s dirt.

The Annapolis nonprofit’s data science team has developed a new artificial intelligence deep learning model for mapping wetlands, with a reported 94% accuracy. This “AI wetlands model” shows hope for what has been decades of challenges for protecting and conserving wetlands, VP for Climate Strategy Susan Minnemeyer told

What are wetlands? Quite literally, they’re areas where water covers soil. The soppy nature of these geographical terrains has always proved tricky to map for organizations like the Chesapeake Conservancy.

“Wetlands are called the kidneys of a landscape or nature’s supermarket because they provide so many ecological services,” said Dr. Kumar Mainali, a lead member of the Chesapeake Conservancy-housed Conservation Innovation Center team responsible for this new and highly accurate deep learning model. “Unfortunately, in North America, we’ve lost about 36.5% of our wetlands since 1900. … Wetlands are important because they clean polluted waters, protect sewer lines, recharge groundwater, stabilize water supplies, regulate climate, provide food, water, timber, and they interestingly mitigate both floods and drought.”

National Wetlands Inventory data hasn’t been comprehensively updated for many years. Mainali said modeling an approach to wetland mapping that can use training data of varying geographies will be useful in modernizing wetland mapping where it is most needed.

The results of this research were recently published in the peer-reviewed Science of the Total Environment. Much of the Chesapeake Conservancy data team’s work was funded by EPRI, or Electric Power Research Institute.

“The institute has had big challenges around wetlands whenever they have new infrastructure projects, since there’s a lot of infrastructure growth in cities, including solar development and renewable energy projects,” Minnemeyer said. “They want to reduce the risk of stumbling upon an environmentally sensitive area before getting to the field work portion of development.”

According to the researchers, unmapped wetlands are a result of old data being manually drawn using aerial photographs. That’s where an AI model comes in handy.