in collaboration with:
United Nations Development Programme
Planning blue-green cities with Artificial Intelligence
Currently, more than half of the world's population lives in cities and the urban population is expected to double by 2050. Intense urbanization has led us to rethink human socio-economic development on a global scale with particular emphasis on the relationship between human beings, their footprint and the environment.
Cities and city regions today are at the forefront in both feeling the effects and fighting to offset the potentially catastrophic effects of climate change.
Cities are the biggest CO2 emitters globally, and therefore it is necessary to redesign their infrastructure, rethink consumption patterns and circularity.
In other words, the question we pose here is there a way to convert what they now expel, as waste or pollution, into raw material to feed new processes of production, and how can we make visible what is now invisible and informal in cities?
This entails innovative strategies of waste management, water conservation and recycling, renewable energy production and trading. It also involves implementing technologies for the filtration and re-metabolisation of air pollution. At the same time, we recognize that in each urban space there are layers of informality which supplement and complement existing public services - whether it is in water catchment, individual waste recycling, or in other forms - like decentralized construction. These dynamics, while an essential part of the tapestry of life in cities, are not always recognized. Yet, effective ways of addressing vulnerabilities demand utilization of the entire human and environmental systems in cities.
We can design resilient cities that use their size and collective energy to create refuge for both humans and displaced wildlife, that promote the emergence of positive microclimate, that replenish depleted water sources and that restore degraded terrains, pushing back on processes such as desertification, land erosion and contamination. This entails innovative strategies of urban re-greening and re-wilding as well as of urban agriculture.
A critical quality of urban planning today it to mobilize collective agency and intelligence to face the challenges ahead. In this way local solutions can be evolved in response to the given challenge.
In this recent collaboration with UNDP, ecoLogicStudio has been testing the potential of Artificial Intelligence to develop a new green planning interface. This planning solution combines the scalability of a sophisticated planning application to the design sensibility and intuitive accessibility of its design interface. This enables a high degree of customisation and evolution to each specific urban application, or urban design solution.
At its core, this application uses sophisticated algorithms to analyse hi-resolution data on urban landscape and infrastructure (mostly available open source) to produce simulated scenarios of sustainable urban development and a new way of urban planning - one that is dynamics, iterative and comprehensive.
These simulations have three key characteristics:
- they are open to multiple external input, that is, all urban stakeholders can interact with several layers of data and see the effects of their actions on the proposed planning scenarios - and what we have used here is satellite imagery and open street maps.
- they are time based and non-linear, and in that sense they enable all stakeholders to appreciate the effects of new policies and strategies systemically, across disciplines and planning regimes, being able to look at urban planning, at the same time as re-greening as well as mitigation of climate impacts.
- they have a powerful visual and morphological output, thus enabling all stakeholders to visually appreciate the simulated urban form across several orders of scale.
As demonstrated by our test run with early adopter cities, such as Aarhus, Tallinn, Barcelona, Caracas, which were then applied in our joint project Vranje, Guatemala and Mogadishu, our design scenarios simulate the evolution of restorative urban networks.
This process questions traditional planning concepts such as zone, boundary, scale, typology and program. Such outdated notions actually constrain the emergence of a truly systemic approach to urbanisation, one that recognises the true nature of contemporary cities as complex dynamical systems - where built environment, and human systems interact regularly with green spaces.
This issue is most evident in the case of Guatemala City.
Applications - Case Studies
Guatemala City is situated on a complex and highly unstable terrain surrounded by mountains and volcanoes, some of which are still active. Its ecosystems, originally very rich in biodiversity, are now made fragile by unchecked urbanisation and, given its climatic zone, the effects of climate change.
In Guatemala City this scenario is exacerbated by a serious lack of waste management. The Guatemala city garbage dump is the biggest landfill in Central America containing over a third of the total garbage in the country. 99% of Guatemala's 2,240 garbage sites have no environmental systems and are classified as "illegal."
Only a new design methodology powered by big data gathering and the production of ad hoc algorithmic design scenarios can deal with such complexity and level of informality.
Our approach creates an interface between bottom up processes of self-organisation, such as the many local waste recycling activities that are emerging out of necessity in the areas closer to the dumping sites, and the strategic decision making that occurs at municipal, national and international level.
Re-wilding Guatemala City
The aim is to find new synergies and direct investments where and when have they have the most potential to engender positive change.
Two proposals emerged in this analysis of Guatemala City: a re-wilding plan foster a new coexistence between human and urban wild animals, and an urban agriculture plan proposing a method to guarantee food security and to employ the impoverishes rural population currently migrating to the city of Guatemala.
Both proposals are sensitive to local conditions while effecting international power relationships.
For instance, the migrating birds populating the green areas of Guatemala City migrate to and from Canada. Therefore, investments in urban re-wilding will have benefit in the biodiversity of Canada.
Migrant workers cross the city on their way to the US-Mexico border. Urban agricultural plans could retain rural workers alleviating the pressure on both Mexico and the US.
Such synergies have the potential to channel significant international funds to local projects improving the life of citizens of Guatemala City.
Similarly in Mogadishu, another case study, land degradation is a key environmental issue and is closely related to desertification, drought and unsustainable livestock and agricultural practices.
The problem is exacerbated by a completely horizontal development of the city and the critical lack of water. Vegetation is so sparse that its restorative effect becomes negligible.
In our proposal a bottom up water collection and filtration network is computed to optimize its performance in relationship to existing urban density and road networks connectivity. This is then reinforced by a large scale re-greening strategy aim at creating dense networks of plants in proximity to the areas of collection, thus promoting the emergence of restorative microclimates and ecological niches.
Crucially here the algorithmic interface allows the simultaneous computation and optimisation of contrasting parameters and the development of a multi-scalar approach.
The urban re-greening strategy has therefore multiple hierarchies. Locally it recognises gaps in the urban vegetation and gives guidance for the planting of trees in optimal locations. At the scale of neighbourhood is optimises the location of water collections points serving the existing buildings. At the urban scale it fosters densifications of the vegetated network to promote the emergence of ecological niches and local microclimates, especially around water collection zones. At the territorial scale it promotes the emergence of a barrier, natural and man-made, to push back desertification and restore some of the abandoned agricultural plots as well as the infrastructural networks of canals and water wells.
Vranje Renewable Energy City Region
The third scenario of this study is the city of Vranje in Serbia. It is a very different kind of urban system compared to the two previously described.
Vranje is a distributed city region, with a small size and little resident population but with far greater opportunities in terms of establishing a new regional network economy. The study recognises opportunities latent in the surrounding territory in the production of renewable energies from sources such as solar, wind, hydraulic and biomass.
Such locally distributed production could give rise to an emergent and integrated renewable energy network capable of generating significant new circular economies and high-level employment opportunities.
In conclusion, we are developing an urban design methodology and related technology that effectively deploys artificial intelligence to the re-greening of global cities and city regions.
The method is scalable and we intend to develop a dedicated urban design application to drastically increase the number of cities that could benefit from this technology.
In order to perfect it we seek to apply it to the design of several more test cities with clear urban design challenges.
This initiative has been developed with the support of the Innovation Facility funding, and is a part of the City Experiment Fund, supported by the Slovak Ministry of Finance.
Project by ecoLogicStudio ( Claudia Pasquero and Marco Poletto )
Commissioned by UNDP ( Lejla Sadiku )
Text: Deep Green Article by Dr. Marco Poletto and Lejla Sadiku.
Slope analysis of the territory surrounding the city of Vranje. The image is a false colour rendering of the mesh derived from NASA’s global Digital Elevation Model.
Animated algorithmic plan. Wildlife flows / urban coexistance. IAAC Introductory Studio. Researchers: Aditya Ambare, Francesco Polvi, Dinesh Kumar V., Chim M Lim Faculty: Dr. Marco Poletto | Faculty assistant: Apostolos Marios Mouzakopoulos
Water Flow Pattern analysis of the territory surrounding the city of Guatemala. The image is a false colour rendering of the mesh derived from NASA’s global Digital Elevation Model.
Vegetation Index for the city of Mogadishu. The image is algorithmically processed from a Google Earth high resolution satellite view of the city to highlight its biotic layer.
Local to municipal waste collection networks in the city of Guatemala. Image algorithmically computed from GIS map, satellite map and DEM model analysis by means of minimal path algorithm. The analysis also takes into account the result of the local waste collection analysis.
Rain water collection analysis for the City of Guatemala. The image is computed through a combination water flow simulation patters and minimal networks on the DEM mesh of Guatemala City topography.
Biotic network analysis for the city of Guatemala. Drawing of the proximity network of existing biotic systems, highlighting areas lacking connectivity and requiring re-greening.
La Republica Alimentaria, simulated plan for extended urban agriculture in Guatemala City. Proposed application of the DeepGreen tool to tackle the issue of food security. The plan illustrates the territory of Guatemala City as a potential for growing the 3 main staple foods for the city. It also simulates the interaction between the plots and the existing urban infrastructure to support the emergence of a local market of produce distributed in key areas of the city where low income population is at high risk of starvation.
Analysis of local water collection and distribution networks in Mogadishu. Image algorithmically computed from GIS map and satellite map analysis by means of minimal path algorithm.
Distributed solar energy network potential on the regional territory of Vranje. Quantitative simulation of total yearly incident solar radiation on the NASA digital elevation model of the region.