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How satellite images and AI could help fight spatial apartheid in South Africa  

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How satellite images and AI could help fight spatial apartheid in South Africa  

Raesetje Sefala grew up sharing a bedroom along with her six siblings in a cramped township within the Limpopo province of South Africa. The township’s inhabitants, predominantly Black people, had inadequate access to colleges, health care, parks, and hospitals. 

But just a number of miles away in Limpopo, white families lived in big, attractive houses, with easy accessibility to all this stuff. The physical division of communities along economic and racial lines, in order that townships are close enough for the people living there to commute to work but too far to simply access essential services, is only one damaging inheritance from South Africa’s era of apartheid.

The older Sefala became, the more she peppered her father with questions on the visible racial segregation of their neighborhood: “Why is it like this?”

Now, at 28, she helps do something about it. Alongside computer scientists Nyalleng Moorosi and Timnit Gebru on the nonprofit Distributed AI Research Institute (DAIR), which Gebru arrange in 2021, she is deploying computer vision tools and satellite images to research the impacts of racial segregation in housing, with the final word hope that their work will help to reverse it.

“We still see previously marginalized communities’ lives not improving,” says Sefala. Though she was never alive through the apartheid regime, she has still been affected by its awful enduring legacy: “It’s just very unequal, very frustrating.”

In South Africa, the federal government census categorizes each wealthier suburbs and townships, a creation of apartheid and typically populated by Black people, as “formal residential neighborhoods.” That census is used to allocate public spending, and once they are lumped along with richer areas, townships are in effect hidden, disproportionately excluding the people living there from access to resources akin to health services, education centers, and green spaces. This issue is usually often called spatial apartheid. 

Raesetje Sefala is deploying satellite images and AI to map out spatial apartheid in South Africa.
HANNAH YOON

Sefala and her team have spent the last three years constructing a knowledge set that maps out townships so as to study how neighborhoods are changing by way of population and size. The hope is that it could help them see whether or not people’s lives in townships have improved for the reason that legal dissolution of apartheid.

They did it by collecting hundreds of thousands of satellite images of all nine provinces in South Africa, and geospatial data from the federal government that shows the situation of various neighborhoods and buildings across the country. Then they used all this data to coach machine-learning models and construct an AI system that may label specific areas as wealthy, non-wealthy, non-residential, or vacant land. 

In 2021, they found that over 70% of South African land is vacant, they usually saw how much less land is allocated to townships than to suburbs. It was a confirmation of the inequalities that they had expected to see, however the staggering quantity of vacant land still took them aback, says Sefala.

Now they’re sharing the info set with researchers and public service institutions, including nonprofits and civic organizations working to discover land that could possibly be used for public services and housing. DAIR plans to make the info let out and accessible on its website from February 2.

“The work matches squarely into our research paradigm to place data using AI into the hands of marginalized groups,” says Gebru. 

While dismantling spatial apartheid may take a lifetime, Sefala hopes to make use of the tools they’ve developed to fuel systemic change and social justice. “We wish the work to push the federal government to begin labeling these townships in order that we will begin to tackle real problems with resource allocation,” she says.

Data for change  

Moorosi, who now co-advises Sefala at DAIR, first hired her on the South African Council for Scientific and Industrial Research (CSIR) in 2018. Sefala “was absolutely good and fully understood the concept of machine learning,” she says. And Moorosi made her realize that she was not alone in worrying concerning the impacts of spatial apartheid and neighborhood segregation.

South Africa is the world’s most unequal country, based on the World Bank. Nearly three many years after the tip of apartheid, its brutal legacy continues to rob hundreds of thousands of Black South Africans of basic rights, including jobs, education, and access to health care. “It impacts every aspect of individuals’s lives,” says Nick Budlender, an urban policy researcher at Ndifuna Ukwazi, a nonprofit that advocates for urban land justice in Cape Town.

Sefala’s work is starting to make its way into the hands of South African institutions and researchers. Earlier this month, DAIR shared its data with a South African policy think tank, the Human Sciences Research Council (HSRC), which is using the data to advise the federal government on budget allocations for HIV treatment programs across the country. “In the event that they don’t know where the townships are—how briskly the population is growing—it makes it difficult for them to allocate resources which might be realistic,” says Sefala.

But perhaps the most important impact her work could have could be to assist provide information to organizations fighting for justice in urban planning, especially within the face of South Africa’s worsening housing crisis. For instance, in Cape Town, essentially the most racially segregated city on the planet, about 14% of households live in informal settlements—unplanned areas without adequate shelter and infrastructure. If a few of the vast tracts of public land were become inexpensive public housing, many individuals wouldn’t need to live in informal settlements, advocates say.  

Nonetheless, the shortage of publicly available information on public land in town perpetuates a government myth that town lacks vacant land.

“Now we have an actual dearth of quality data,” says Budlender, and that makes it rather a lot harder to advocate for the usage of public land to construct public housing and services like hospitals. Last September, after five years of research, Ndifuna Ukwazi launched a digital interactive map, often called the People’s Land Map, displaying 2,700 parcels of vacant and underutilized public land in Cape Town. 

Its aim is to reveal that there’s ample public land available to assist address the housing crisis. “When we’ve got called for the event of inexpensive housing, the federal government has often responded by saying that there isn’t land available. By developing the map we’ve got conclusively proven that this isn’t the case,” says Budlender.

Sefala says that they hope to share their data to support the work of Ndifuna Ukwazi. And Budlender is happy about the probabilities it could open. “It offers an actual opportunity to trace and show evidence on how townships are changing, and to shape policy,” he says. “Policy is just ever nearly as good as the info that it relies on.” 

Nowadays Sefala travels throughout South Africa, giving talks to policymakers, advocates, and students. When she walks through the streets of Johannesburg, she often stops and stares at the massive gated houses and ponders the difference between townships and wealthy neighborhoods.

“Townships are terribly poor, and it was a part of my reality,” she says. “But I’m joyful doing something about it.”       

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