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Artificial intelligence and augmented urban design

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In the field of urban design, constraints continue to evolve as society’s expectations advance. The result: the data available to professionals is exploding. Local shops, noise pollution, sun exposure, compliance with environmental standards … There are many measurable factors that have significant impacts on the work of urban planners and architects. In fact, their quality is often measured by their ability to find the right balance between human needs and economic imperatives.

In this context, the milestones for project feasibility are often characterized by a highly experimental and not very rewarding approach. In the face of this imbalance, artificial intelligence (AI) appears to be the missing link that allows human intelligence to push the boundaries by increasing analytical capabilities.

Although the benefits of this technology are generally accepted, it faces legitimate concerns among urban design professionals. The latter is afraid of being replaced by opaque and soulless algorithms. They often fail to conceptualize the concept of an augmented human being. This situation illustrates the need to democratize AI, but also to redefine the word intelligence in certain contexts. In our societies, expertise generally corresponds to the level of knowledge a person possesses in a field. With the advent of the augmented human, we will witness a paradigm shift where close cooperation between different forms of intelligence, human and machines, tends to multiply. This collaboration is already effective with artificial intelligence.

More centralization for further improvement

Today’s feasibility stage requires an interpretation of the local urban plan, and then a determination in a program that most often leads to results that are not closely related to the contracting authority. It is also necessary to take into account a large number of different data: building heights and dimensions, the proportion of green areas, the programmatic study and capacity, the profitability of the project required by the contracting authority and, finally, the relationship between purchase prices. of the land and what can be established there.

This dissemination of data, at multiple levels, requires the adoption of centralized tools that allow urban planners and architects to access it quickly and then analyze it effectively. Therefore, these platforms can simplify the understanding of a project by allowing it to be considered from different angles but also according to several criteria.

Once information becomes central, it should be used normally. This is where AI comes in to make this exploit possible through ordering and creating multiple correlations. Let’s take a simple case: when a contracting authority requests a site to be densified, the AI ​​can automatically and quickly calculate as many housing units as possible by taking into account the parking area or the imposed green space quota. This automated result then feeds into the strategic thinking of the business expert. Thus, the two intelligences enrich each other in a hybrid process that sets new working frontiers.

Other examples: sunlight studies, heat island detection, and noise pollution calculation. In the feasibility stage, the AI ​​can automatically calculate these elements according to various parameters: brightness, crowding in nearby streets, speed limits on streets, and so on.


Business expert increase, not replace

Remember, AI intelligence relies on the data available to it. Without data, AI cannot learn. The business expert therefore retains its central role in urban design because, for the time being anyway, only human intelligence can understand the irrational needs expressed by other humans in terms of urban design.

Rather than being a threat that tends to replace the professional expert, AI presents scenarios and renders dimensions difficult to comprehend by traditional means. Thus, the contribution of new information makes it possible to broaden the views of the business expert, and does not impose an accurate view based exclusively on data. We should consider AI as a multi-entry interface that brings together universes that were not necessarily intended to intersect. With it, the business expert moves the boundaries between the once narrow disciplines, and makes them work together.

When AI calculates sunlight curves, for example, it puts humans themselves into a monitoring logic that allows them to assess the suitability of what the algorithm is giving them. It is about gradually adopting a new way of working that combines the strengths of all and thus makes it possible to push back the boundaries during the viability phase.

This ability of AI to not only translate requirements but also to manipulate and find logical paths in large data sets increases the capabilities of the business expert. It becomes easier for him to quickly correlate environmental and economic data, obtain metrics beforehand, give an accurate answer to the promoter about the profitability of the project, etc.

Thus, the integration between AI and the business expert makes it possible to achieve more coherently representative scenarios, on which the human being, the only final decision-maker, can rely. At first, to assess the development potential of the project, and later during the study phase. Thus, the impact of AI on the value chain is manifold.

Optimization is the keyword of AI that carries with it a certain irony. Indeed, through a mathematical instrument, artificial by definition, and whose purpose is therefore rationalization, human considerations (volume, sunlight, green spaces, etc.) that affect sensitive life can intervene. This technology, which is based on real-world data, allows humans to consider elements of profitability that go beyond the financial dimension alone. In this way, it enhances the quality of life.

Tribune By Mathieu Piat, Leading Architect and Builder at Prodware

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