Catherine Nakalembe walked Eastern Africa's farms for six years. Then she put the cameras on other people's helmets.
Uganda, Kenya, and across Eastern Africa · Catherine Nakalembe
Published August 6, 2026
Satellites could photograph Kenya's farmland but could not tell maize from beans, so a Ugandan geographer, thirteen colleagues, and a network of motorcycle riders went and collected the answer from the roadside.
The story
The person and the place
Catherine Nakalembe is a geographer, originally from Uganda, who leads the Africa program for NASA Harvest, the United States space agency's food security and agriculture program.
The problem Catherine cared about
Satellites photograph Africa's farmland from roughly 350 miles up. What they could not do was tell her whether the crop below was maize or wheat, and without that, no model could work out how a field would hold up against a heat spike or a long wet season. The pictures were never the hard part. The labels were.
The agency moment
So she went and got the labels on foot. Every July and August from 2010 to 2016 she crossed small farms, often no more than three acres each, writing down what was growing where. "I walked so many miles, I was in such great shape," she says of those years, and "It was time-consuming and so hard, but it's what was needed." One person cannot walk a country, so she stopped trying to. She put the cameras on other people's helmets instead, working with institutional partners and a citizen science network of riders who carried GoPros down ordinary roads.
What changed
Across Kenya's 2021 and 2022 long-rain seasons, the riders gathered the pictures and Nakalembe and thirteen colleagues built the deep learning pipeline that read them, turning roadside photographs into 4,925 validated crop-type data points. Kenneth Mwangi, Jane Kioko, and Christopher Atsianzale Wakhanala are on that author list beside her. They published the dataset open access in 2025, free for anyone to use, and maps built this way feed yield estimates, which tell a government months ahead whether it is heading for a shortfall.
Where it led
She has kept arguing that the tools have to fit the places they are used. Models built elsewhere, she told Rest of World, "often also fail to account for the contexts of the Global South, including high internet costs, limited bandwidth, and a lack of labeled training data."
"So imagine face detection software or Google Street View, but in this case, we're looking for crops, not buildings or faces." — Catherine Nakalembe, Social Science Space
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