Machine learning is at the heart of QTrees, an innovative project designed to optimise urban tree care in Berlin. The project directly addresses the challenges of climate change by predicting drought stress in the city's extensive network of over 800,000 street trees. By combining data from diverse sources, including soil moisture sensors, comprehensive weather data, and high-resolution satellite imagery, QTrees provides actionable insights for Berlin's green infrastructure teams. This data-driven approach enables the development and implementation of precise watering schedules, ensuring the health and resilience of the urban forest whilst significantly reducing water waste.
Developed by CityLAB Berlin with support from the Federal Ministry for the Environment, QTrees offers a valuable blueprint for other European cities facing similar environmental pressures. Its success demonstrates the potential of advanced urban analytics to proactively manage green spaces, enhance urban liveability, and contribute to climate change adaptation strategies. The project's scalable approach, leveraging readily available technologies and open data sources, makes it readily transferable to other urban environments, offering a practical and impactful solution for sustainable urban development across Europe. By minimising water consumption and safeguarding urban tree populations, QTrees contributes to building more resilient and environmentally responsible smart cities.