How Artificial Intelligence and Open Data can re-imagine the way we design our cities
Fecha de la noticia: 18-03-2024
After months of new developments, the pace of advances in artificial intelligence does not seem to be slowing down - quite the contrary. A few weeks ago, when reviewing the latest developments in this field on the occasion of the 2023 deadline, video generation from text instructions was considered to be still in its infancy. However, just a few weeks later, we have seen the announcement of SORA. With this tool, it seems that the possibility to generate realistic videos, up to one minute long, from textual descriptions is here.
Every day, the tools we have access to become more sophisticated and we are constantly amazed by their ability to perform tasks that once seemed exclusive to the human mind. We have quickly become accustomed to generating text and images from written instructions and have incorporated these tools into our daily lives to enhance and improve the way we do our jobs. With each new development, pushing the boundaries a little further than we imagined, the possibilities seem endless.
Advances in Artificial Intelligence, powered by open data and other technologies such as those associated with the Web3, are helping to rethink the future of virtually every field of our activity: from solutions to address the challenges of climate change, to artistic creation, be it music, literature or painting[6], to medical diagnosis, agriculture or the generation of trust to drive the creation of social and economic value.
In this article we will review the developments that impact on a field where, in the coming years, interesting advances are likely to be made thanks to the combination of artificial intelligence and open data. We are talking about the design and planning of smarter, more sustainable and liveable cities for all their inhabitants.
Urban Planning and Management
Urban planning and management is complicated because countless complex interactions need to be anticipated, analysed and resolved. Therefore, it is reasonable to expect major breakthroughs from the analysis of the data that cities increasingly open up on mobility, energy consumption, climatology and pollution, planning and land use, etc. New techniques and tools provided by generative artificial intelligence combined, for example, with intelligent agents will allow a deeper interpretation and simulation of urban dynamics.
In this sense, this new combination of technologies could be used for example to design more efficient, sustainable and liveable cities, anticipating the future needs of the population and dynamically adapting to changes in real time. Thus, new smart urban models would be used to optimise everything from traffic flow to resource allocation by simulating behaviour through intelligent agents.
Urbanist.ai is one of the first examples of an advanced urban analytics platform, based on generative artificial intelligence, that aims to transform the way urban planning tasks are currently conceived. The services it currently provides already allow the participatory transformation of urban spaces from images, but its ambition goes further and it plans to incorporate new techniques that redefine the way cities are planned. There is even a version of UrbanistAI designed to introduce children to the world of urban planning.
Going one step further, the generation of 3D city models is something that tools such as InfiniCity have already made available to users. Although there are still many challenges to be overcome, the results are promising. These technologies could make it substantially cheaper to generate digital twins on which to run simulations that anticipate problems before they are built.
Available data
However, as with other developments based on Generative AI, these issues would not be possible without data, and especially not without open data. All new developments in AI use a combination of private and public data in their training, but in few cases is the training dataset known with certainty, as it is not made public. Data can come from a wide variety of sources, such as IoT sensors, government records or public transport systems, and is the basis for providing a holistic view of how cities function holistic view of how cities function and how and how their inhabitants interact with the urban environment.
The growing importance of open data in training these models is reflected in initiatives such as the Task Force on AI Data Assets and Open Government, launched by the US Department of Commerce, which will be tasked with preparing open public data for Artificial Intelligence. This means not only machine-readable formats, but also machine-understandable metadata. With open data enriched by metadata and organised in interpretable formats, artificial intelligence models could yield much more accurate results.
A long-established and basic data source is OpenStreetmap (OSM), a collaborative project that makes a free and editable map of open global geographic dataavailable to the community. It includes detailed information on streets, squares, parks, buildings, etc. which is crucial as a basis for urban mobility analysis, transport planning or infrastructure management. The immense cost of developing such a resource is only within the reach of large technology companies, making it invaluable to all initiatives that use it as a basis.
More specific datasets such as HoliCity, a 3D data asset with rich structural information, including 6,300 real-world views, are proving valuable. For example, recent scientific work based on this dataset has shown that it is possible for a model fed with millions of street images to predict neighbourhood characteristics, such as home values or crime rates.
Along these lines, Microsoft has released an extensive collection of building contours automatically generated from satellite imagery, covering a large number of countries and regions.
Figure 3: Urban Atlas Images (OSM)
Microsoft Building Footprints provide a detailed basis for 3D city modelling, urban density analysis, infrastructure planning and natural hazard management, giving an accurate picture of the physical structure of cities.
We also have Urban Atlas, an initiative that provides free and open access to detailed land use and land cover information for more than 788 Functional Urban Areas in Europe. It is part of the Copernicus Land Monitoring Serviceprogramme, and provides valuable insights into the spatial distribution of urban features, including residential, commercial, industrial, green areas and water bodies, street tree maps, building block height measurements, and even population estimates.
Risks and ethical considerations
However, we must not lose sight of the risks posed, as in other domains, by the incorporation of artificial intelligence into the planning and management of cities, as discussed in the UN report on "Risks, Applications and Governance of AI for Cities". For example, concerns about privacy and security of personal information raised by mass data collection, or the risk of algorithmic biases that may deepen existing inequalities. It is therefore essential to ensure that data collection and use is conducted in an ethical and transparent manner, with a focus on equity and inclusion.
This is why, as city design moves towards the adoption of artificial intelligence, dialogue and collaboration between technologists, urban planners, policy makers and society at large will be key to ensuring that smart city development aligns with the values of sustainability, equity and inclusion. Only in this way can we ensure that the cities of the future
are not only more efficient and technologically advanced, but also more humane and welcoming for all their inhabitants.
Content prepared by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalization. The contents and views reflected in this publication are the sole responsibility of the author.
