AI tool developed to identify informal settlements

SANSA, AWS, ZINDI COLLABORATE TO DEVELOP AI TOOL THAT IDENTIFIES INFORMAL SETTLEMENTS

The South African National Space Agency (SANSA) has collaborated with Amazon Web Services and data science platform Zindi to develop an artificial intelligence (AI) tool that can identify informal settlements using SANSA’s satellite data.

SANSA said in a statement on its website that the COVID-19 pandemic had made it more critical than ever for the government to identify countrywide informal settlements. The agency explained that knowing where informal settlements are located would have help the government make evidence-based policies which would ensure that all people have access to the services they need to manage the crisis.

SANSA Managing Director for Earth Observations said the idea was to get data scientists to work on a potential solution that SANSA could use to optimise its mapping processes.

Between 12 and 15 -- June as part of the SANSA AWS Informal Settlements in South Africa hackathon by Zindi -- 184 data scientists from 34 countries used SPOT satellite imagery provided by the agency to create the models.

The data scientists were also given access to powerful computing capacity through virtual machines provided by Amazon Web Services.

SANSA provided training data that had information about informal settlements around Johannesburg in Gauteng, and challenged the data scientists to create models that can find informal settlements in KwaZulu-Natal. The winning model, created by Raphael Kiminya, a data scientist from Kenya, found informal settlements in KwaZulu-Natal that manual labelling by SANSA data scientists did not flag in the training data.

Zindi Africa CEO Celina Lee explained that the informal settlement that it picked up on, as an example, was a very small area that was surrounded by other homes, but you start to recognise it when you look at it more carefully. “What’s nice is that the model can pick up on what the human eye might just scan right over and not notice,” added Lee.

These models won’t be replacing humans anytime soon; they remain tools developed to make the work of technicians easier, by pointing users towards where to look for informal settlements. “Right now, with the models that we have, it will almost be like a heatmap with different probabilities indicating where an informal settlement is likely to be,” she said.

Lee said it was exciting to work with SANSA on this project as it unlocked opportunities for many African data scientists to showcase their talents. The hackathon also illustrates the wealth of data that SANSA has to offer data scientists on the continent.“We would love to work with SANSA to tackle other problems. There is such great potential in SANSA’s data to generate insights in a quicker and more efficient way using machine learning,” said Lee.ai