As part of the Edge 50 list, Apolitical is publishing a series of articles exploring a selection of these bold initiatives to learn more about the stories and strategies behind them.

Created by Apolitical in partnership with the Mohammed Bin Rashid Centre for Government Innovation, this list is an invitation to look beyond the usual playbook and ask what becomes possible when governments are willing to think boldly about today’s biggest problems.

Apolitical spoke with Fábio Duarte, Associate Director of Research and Design at Senseable City Lab, an initiative between the Massachusetts Institute of Technology (MIT) and the Municipality of Rio de Janeiro, which uses advanced sensing tools to map informal settlements (favelas).

Most smart city projects start with researchers presenting technology to government. Favelas 4D reversed that: the idea came from Rio's Head of Urban Planning, who approached MIT with a problem to solve. Rather than flying over favelas with aerial scanners, the project uses ground-level mapping that requires researchers to ask residents’ permission before entering, putting communities in charge of what gets mapped and how.

What challenge was the Senseable City Rio project originally trying to address, and how did your understanding of the challenge change once the work began?

Our main challenge when we started Senseable City Rio was to spatially understand the informal settlements common in Asia, Latin America and Africa. When we think about cities in the field of urban planning, most of the time we deal with formal cities that have legislation, policies, and very precise regulations about what and how to build. But many new cities emerge informally, and this is the case with favelas. We realised that traditional tools for mapping cities are very poor at mapping informal settlements. They do not provide the information that residents actually need. Our initial question was whether we could use new technologies to map informal settlements in ways that would help address problems that matter to residents.

One thing that surprised us was how much the communities themselves were already experimenting with informal mapping. During the pandemic, for example, one favela organised local campaigns to identify which areas were most affected by illness because authorities were not entering the community. So residents were already finding their own ways to map and understand their environment. That discovery shaped our approach and reinforced the idea that research needed to be collaborative from the beginning.

What does Sensible City Rio look like in practice beyond the headline idea?

The Senseable City Rio lab is based at FIRJAN, the Industry Federation of the State of Rio. They have a strong social and cultural branch that runs cultural programmes and technology workshops. The lab has around ten people, including researchers and visiting students. But the work goes beyond the physical lab. If you visit, you might see people working across the building because once projects start, they expand beyond the original space.

The project's outputs also exist at very different levels. On one side, we have very high-technology outputs. For example, we use handheld laser scanners to create extremely detailed 3D models of favelas. These generate what we call “point clouds”, which are precise spatial data models. One of these datasets was exhibited at the Venice Biennale in 2025. On the other side, we have residents collecting environmental data themselves. In one favela, residents measure heat stress and air quality daily using sensing devices that they helped assemble. Every week, we meet to discuss the data and how it can be used. The high-tech outputs and the community work may look very different, but they are both part of the same project.

The project involves collaboration between researchers, city officials and local residents. How were decision-making and roles shared?

One thing that we make clear when MIT enters a collaboration is that research is led by us. If there is no strong research value, there is no point in doing the project. However, that does not mean the ideas come only from researchers. The heat stress project is a good example. It didn’t start as a planned research question. It emerged during a community visit when someone overheard our discussion and said that heat was a major problem. That started another conversation with residents and eventually with the municipality.

If you don’t have your foot on the ground in cities, you may produce good academic research with very little impact.

Because the municipality was present during these discussions, it became clear to everyone that this was an important issue worth exploring. The city was very supportive, even though the research direction had shifted from the original plan. That alignment between researchers, residents and city authorities made the collaboration possible.

What felt risky or counterintuitive about the approach you took?

One issue we had to explain many times was why we were using handheld laser scanners rather than simply flying over favelas with aerial scanning. Cities like Rio already collect aerial lidar data by flying over the city. It covers a large area quickly, but aerial scanning misses many details in dense neighbourhoods with narrow streets or covered pathways. Handheld scanners are slower but produce much higher-quality data.

More importantly, there is a social dimension. Why should anyone fly over someone else’s community collecting data without asking permission? With handheld scanning, we have to talk to residents first. We ask whether we can enter, which areas we can map and which technologies we can use. For example, some communities allow laser scanning but do not want cameras. Finding the right balance between technological possibilities and social consent takes more time, but it produces better outcomes.

Did you face resistance along the way, and how did you manage it?

Yes, resistance still happens. When we first proposed using lidar scanning in some favelas, the community response was “no”. Residents said this was not what they needed. Instead, we started working together on environmental sensing, measuring heat stress and air quality. Over time, through regular collaboration and discussions about the data, residents began to see that accurate spatial maps might also be useful. Trust was built through collaboration rather than persuasion. Now, after months of working together, the community itself is suggesting that lidar mapping could help link environmental data with spatial information. So the project evolved gradually through engagement. It was not about convincing people immediately but about building understanding together.

What early signals suggest that this approach might work?

One promising signal comes from comparing different types of spatial data. The city of Rio has aerial lidar scans of the entire city, including favelas. But these datasets are less detailed than ground-based scans.

We are now comparing our terrestrial scans with the city’s aerial scans to see where they match and where they differ. If we can calibrate the aerial data using the more precise ground data, the city’s dataset becomes much more useful for understanding population growth and urban change. For instance, some favelas have very narrow alleys (less than one meter wide), and often covered alleys; both of which are not captured by aerial imagery. Thus, having ground scanning allows us to understand the complexities of these spaces, which can inform urban planners and designers. Another important signal comes from community engagement. Residents who collect environmental data often feel proud of their work. They understand the devices, the data and why it matters. If the city later proposes interventions to address heat stress, acceptance may be higher because residents were involved in collecting and understanding the data from the beginning.

What advice would you give public servants who want to try similar approaches?

I would give three pieces of advice. First, put aside some of the cognitive tools that planners have traditionally used. Try experimenting with new technologies such as environmental sensors, lidar scanning or generative AI. Tools shape how we think. Second, recognise that these tools are becoming cheap and accessible. Community members often have access to the same technologies.

Residents are not just stakeholders. They are collaborators.

Third, keep your foot on the ground. Work directly with communities rather than relying only on planning offices or institutions.

New technologies are becoming technological equalisers. They reduce the gap between experts and residents.

That opens new possibilities for collaboration in cities.