Civic AI Toolkit: Connected urban forest blueprint
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Civic AI Toolkit: Connected urban forest blueprint

Trees provide us with vital services. New York’s urban forest is estimated to provide more than $100m annually through flood mitigation, carbon sequestration, pollution removal and energy use reduction. In the UK, parks and green spaces are estimated to save the NHS £111m per year. Yet across the world, local governments and charities are struggling to maintain urban forests, let alone match the scale of planting needed to meet net-zero carbon targets. In 2019, the UK was 71% short of its urban afforestation targets. This is partly because we struggle to accurately measure these benefits.

Connected urban forest - overview layout.png

This blueprint uses augmented collective intelligence to address the following key challenges:

  • Data collection - cost and resource required to collect data about the holistic benefits of urban trees.
  • Participation - engaging community organisations and citizens to help collect data and maintain trees.
  • Measuring impact - accurately measuring the impact and wider benefits of the urban forest.

How it works in practice

In this use case, people and machines work collaboratively to locate and monitor the characteristics and condition of urban trees, regularly contributing data to an open tree registry. A combination of remote sensing (e.g. through satellites), on the ground sensors, and data collected by citizens provides data streams that help to create a digital twin, of the urban forest.

Using open data standards, the data is made accessible to a range of stakeholders through application-specific dashboards and open application programming interfaces (APIs). Machines analyse the data to determine individual maintenance and caring needs, with virtual bots alerting citizens about nearby trees that require care through a dedicated community app .

Citizen- and sensor-collected data are analysed to model actual impacts or simulate potential impacts. For example, their contribution to flood mitigation, local cooling, biodiversity support, improved well-being, or to support outcomes-linked financing.

This use case involves significant interaction between machines and people in the collection, processing, and verification of data on tree characteristics, yet it is important that human oversight and agency is retained over any automated decisions and simulations at later implementation stages. Mechanisms for engagement should encourage inclusive participation as well as ensuring that benefits of urban trees are widely distributed to all community members. For example, all autonomous AI agents and user interfaces on apps and digital dashboards should be designed to the latest accessibility standards and the data collection protocols should follow principles of data justice. The optimal combination of citizen-generated data and physical sensors should be continuously reviewed to minimise the material and carbon footprint of the system.