Can a city become intelligent simply by adding more sensors, more data and more advanced technology?

Urbanologist, Smart and Sustainable Cities Specialist and Urban Intelligence Researcher Serdar Aslan argues that it cannot. In his model, technology and artificial intelligence are multipliers. The real challenge is turning a city's memory, human experience and reliable present-day data into better future decisions.

The digital transformation that accelerated after the pandemic has entered a new phase. Artificial intelligence is no longer simply another layer of digitalization. It is changing how systems work across production, education, health, mobility and public management.

At the same time, 5G is spreading, 6G is advancing, and cities are producing more real-time information through IoT systems, sensors, satellites and connected infrastructure. As technology changes the way we live, it will also change the way cities are governed.

For years, good urban management has been discussed in terms of governance, participation, planning, sustainability and capable leadership. None of these have become less important. But the AI age adds another requirement: the ability to bring a city's memory, local experience, stakeholders, reliable data and artificial intelligence together when deciding what to do next.

For Aslan, this is where the idea of urban intelligence begins to matter.

Beyond tech project

I recently spoke with Serdar Aslan, who has spent years working on cities, local governments and the future of urban management. His work combines long-term local and regional field experience across Türkiye with active involvement in international platforms focused on smart and sustainable cities and urban governance.

He is also the founder of CityLab Smart Cities Information and Innovation Center, the author of Smart Cities – Smart Governance – Smart Lives: 2023–2030–2050, and the founding president of the Beşiktaş Urban Association. That background matters because Aslan's relationship with cities is not limited to theory.

Urban life, urban belonging, the right to the city, participation and local government have shaped his fieldwork for years. They also help explain why his approach to technology begins not with the machine, but with the city itself.

At the heart of CityLab's approach is a distinction Aslan has defended for a long time:

A smart city should not be reduced to an engineering or technology project.

For CityLab, a smart city is not simply the sum of sensors, software, cameras or digital municipal services. It is a multidisciplinary way of life and management that brings together social sciences, the environment, culture, planning, governance, the circular economy, waste separation, recycling, zero waste, resource efficiency, climate policy and sustainability.

One idea from Aslan's book captures the logic behind this approach: Smart cities are shaped by their geography.

A model designed for one city cannot simply be transferred to another city and expect the same result. Climate and topography change. So do water resources, lifestyles, production patterns, culture, population dynamics, transport behavior, economic functions and risk profiles. Cities are not copies of one another. Their smart-city models cannot be copies either.

Aslan now extends the same idea into urban intelligence: every city's intelligence must also be shaped by its own geography, memory and experience.

Accumulation, technology

At the center of the CityLab Urban Intelligence approach is a simple formula:

Urban Intelligence = Urban Accumulation × Technological Multiplier

"Urban accumulation" includes urban memory, human and local experience, management vision, planning, shared urban wisdom, sustainability and the practical knowledge a city has built over time.

The technological multiplier consists of reliable data, IoT, sensors, digital technologies and AI. The key word is multiplier. Neither technology nor artificial intelligence is intelligence on its own; they amplify the knowledge a city already holds.

A city can install thousands of sensors and generate millions of data points. AI can process them at extraordinary speed. But if the data are incomplete, unreliable or detached from the city's actual context, even the most advanced system will produce limited value.

And there is another limitation. Artificial intelligence can only process what has already been recorded; it cannot reach the things a city remembers but never wrote down.

That leads to another proposition Aslan often uses: Experience is bigger than AI.

This isn't about being anti-AI. It's about putting AI in its rightful place alongside human knowledge. Think of someone who has lived in the same neighborhood for decades and knows, without looking at a map, exactly where rainwater pools every time it pours. Or a farmer who can look at a hillside and know, from years of observing the land, which crop is most likely to thrive there.

AI can process enormous amounts of data. But human experience gives that data context, meaning and lived understanding.

Someone who lived through a disaster carries a similar kind of knowledge: they may remember which route actually worked once normal systems failed. None of this automatically exists in a database. If the experience was never captured, the digital system cannot begin with it.

For Aslan, urban intelligence emerges when human experience and managerial judgment meet AI's analytical power. The urban manager of the future will therefore need more than a dashboard. They will need to identify reliable data, understand the city, ask the right questions and combine AI with local experience.

Memory

Memory no longer serves only as an archive, and this also changes the way we use urban memory.

Traditionally, memory work records when a fountain was built, who commissioned it and what architectural features it has. If an area once contained market gardens, the historical record may note that they were once there.

All of this is valuable. Urban intelligence goes one step further. Why did something exist in that particular place? How did it function? And what can today's city learn from it?

A former agricultural area, for example, can be read through its soil, water, slope and microclimate rather than only through an old photograph. A historic water structure can reveal not only architectural history, but also how water was collected, distributed and shared.

Memory is therefore no longer simply an answer to "what existed?” It also asks why it existed, how it worked and what it can teach us about the future.

‘An old fountain’

One small detail in Istanbul's water heritage helps explain Aslan's way of thinking.

Consider the old Saka Fountain.

The name itself embodies multiple layers of urban memory. From the Ottoman era to the early Republic, water carriers (sakas) were part of the daily system that completed the infrastructure by transporting water from fountains to homes, while also fostering social trust and communication within the neighborhood.

But there is another layer. Built into the upper central part of the fountain is a feature that allowed finches, also known as "saka" in Turkish, to drink water as well. It is a small detail, and it points to something bigger.

What struck me while discussing the fountain with Aslan was that he did not stop at its date, architecture or name. His question was different: "What kind of urban thinking produced these functions?”

The fountain remembers a system in which water was carried through the city. Yet one of its design details also suggests an understanding in which humans were not the only users of urban space.

If a public water structure considered other living beings as well, then it carries more than architectural information; it carries an approach to urban life. People, animals, water, air, soil and ecosystems share the same city. Urban intelligence does not simply preserve such a detail as a historical curiosity. It tries to extract the principle behind it.

That does not mean we should copy an old fountain; it means that a useful idea from the past can be translated into present conditions.

A principle once expressed in a public water structure designing urban space with other living beings in mind can today influence parks, squares, green infrastructure or public water systems.

This is what it means to turn memory into intelligence.

Fields city remembers

Aslan applies the same logic to Istanbul's former production landscapes.

The memory of strawberry growing in parts of Beykoz or around Levent, the market gardens along Istanbul's historic walls, and Silivri's well-known wheat and grain-producing landscape are not merely nostalgic stories. For urban intelligence, they are information. Not simply what was grown, but why it could be grown there.

Soil conditions, water access, sunlight, humidity, wind and microclimate can all help explain why one place supported a particular form of production while another did not.

There is also the knowledge accumulated by people who worked those landscapes. That matters because food security is no longer an abstract concern. The pandemic reminded cities how quickly supply chains can be disrupted. Future urban food systems may therefore need combinations of peri-urban farming, controlled-environment agriculture, vertical farming and soilless production.

But looking only at today's vacant-land map may not be enough. Urban intelligence first asks the city: Where were you able to produce food in the past and why?

Historical knowledge can then be combined with present-day soil analysis, water availability, temperature, land use, transport data and climate projections. AI becomes a powerful analytical tool.

But one of its most valuable inputs may still come from the memory of someone who knew that land before the sensor arrived. Technology can expand the data, but experience gives it context.

Districts matter

The district scale matters because metropolitan or provincial narratives naturally tend to highlight the best-known monuments, personalities and events. This is also where Aslan's proposal for Urban Memory Centers in every district becomes significant. His aim is not simply to create another archive.

Yet much of a city's practical memory survives below that level.

A vanished neighborhood fountain.

A buried stream.

A former market garden.

A local crop.

An evacuation route once used during a disaster.

The path floodwater followed.

A wind corridor known by residents.

Or an experience remembered by one generation but never digitally recorded.

Aslan argues that district-level memory centers could capture this hidden layer before it disappears. The information can then be verified, geolocated, digitized, and connected to contemporary urban data. For Aslan, this is the crucial distinction: the memory center should not end with preservation.

The knowledge collected there should find its way back into the everyday choices a city makes, from planning public spaces to preparing for climate and disaster risks.

Memory, in other words, stops sitting on a shelf and starts working for the city.

History deepening intelligence

In newer urban areas, the system may rely more heavily on present-day sensors, mobility data, energy use, environmental measurements and digital infrastructure. This is also why Aslan believes urban intelligence takes on a different dimension in cities with deep historical layers.

Ancient cities add another resource: Centuries of accumulated experience.

Historic water systems, production patterns, disaster memory, trade routes, neighborhood structures and local climate knowledge can enlarge the decision base once properly structured.

Technology then does not only read the present. It can help a city use what it learned in the past. Heritage, in this sense, is not simply something to preserve. It is also information capital.

In Aslan’s words: "Geography shapes the smart city; memory deepens urban intelligence.”

More than digital twin

Digital twins are becoming increasingly important in smart-city planning. Modeling how a city's roads, buildings and utilities actually function can be extremely useful for traffic, infrastructure, energy, water and disaster scenarios.

But Aslan asks another question: Where is the memory of the city we are modeling?

A digital twin may represent today's road or water network. Urban intelligence also seeks to understand, when relevant, how that road functioned during an earlier disaster, what was once produced on that land or what residents know that no sensor has captured.

A digital twin can therefore be a powerful instrument of urban intelligence.

It is not urban intelligence itself.

No single model

CityLab does not classify cities only according to the legal status of their municipalities. Because Aslan’s starting point is simpler: What kind of city are we dealing with?

A heritage city, an industrial center, an agricultural district, a coastal settlement, a tourism destination and a dense metropolitan core may all require different priorities.

Beşiktaş and Silivri make a great example. Both are districts within Istanbul's metropolitan system, yet their urban realities differ greatly.

Beşiktaş must manage intense daily population movement, mobility, public-space pressure, culture, commerce and historic urban fabric, whereas Silivri places greater weight on agricultural land, food production, rural-urban relations and soil and water resources.

An industrial city may place more weight on air quality, logistics and energy. A tourism city may need to focus on seasonal population, water demand, waste management and carrying capacity.

CityLab therefore considers geography, historical accumulation, economic function, population dynamics, risk, natural resources and local needs when defining urban typologies.

The aim is not to impose the same smart-city package everywhere but to build the intelligence model from the city's character itself.

The technology and even the AI infrastructure may look the same from one place to the next. What differs is the question each city actually needs to ask.

From idea to system

Aslan's work becomes more concrete as it moves from being an idea toward a measurable system. He does not want urban intelligence to remain only as a conceptual proposition.

Developed within CityLab, CityLab Score AI is an AI-supported assessment and decision system designed to read cities and local governments through multidimensional criteria, identify gaps and reveal transformation priorities.

Its purpose is not simply to create another ranking. The aim is to evaluate different city and local-government typologies within their own conditions and determine where transformation is most needed.

In that sense, Score AI connects the philosophy of urban intelligence with measurement, prioritization and action.

Aslan's ambition goes even further. The next step is the CityLab School of Urban Intelligence.

He does not want urban intelligence to remain the knowledge of one researcher or one institution.

The aim is to create a field that can be researched, systematized, questioned, taught, developed and transferred to future generations.

There is a symmetry here.

If cities need mechanisms to transfer their memory to future generations, the knowledge of how to read cities, use data and combine human experience with AI also needs to be transferred.

CityLab's multidisciplinary understanding of smart cities is therefore evolving, through sustainability, urban memory, local experience, reliable data and AI, into a broader school of thought around urban intelligence.

Türkiye's cities

What Türkiye’s cities can tell the world lies in the thousands of years of urban experience they carry, shaped by different civilizations, water systems, production landscapes, routes and ways of life.

Within the same country are dramatically different climates, geographies, economic functions and urban types. Türkiye therefore does not have to remain only a user of smart-city models developed elsewhere. It can also develop new urban-management models from its own geography, memory and local experience.

This is the wider significance of the work Serdar Aslan and CityLab are developing.

The aim is not to replace the past with technology. Aslan's argument is almost the opposite: use technology to make accumulated experience useful again.

Urban memory should not remain a source of nostalgia and human experience should not be treated as a rival to AI. Both can provide the context that data alone cannot. Sustainability, meanwhile, has to remain part of how a city makes those choices.

The most successful cities of the future may not be those with the greatest number of sensors, nor necessarily those with the most AI applications.

The difference may be made by cities that know what they know, understand what they have experienced and know why they are using technology.

AI can generate powerful forecasts about the future. But if a city is to become genuinely intelligent, it must also be able to learn from its past.

Perhaps urban intelligence can be described most simply as this:

Remember the past, read the present correctly and combine human experience with the power of technology to manage the future better.