From satellite to street: Earth embeddings for local environmental decisions

a busy road can be much higher than in a park a few hundred metres away. Yet the national forecasts used to warn the public uses grid squares several kilometres across. Long-term exposure to air pollution is linked to the equivalent of around 30,000 deaths a year in the UK. Many of the decisions that matter are made locally: where to place a monitor, when to warn people with asthma, or whether a low-emission zone has worked. AI models trained on satellite and weather data could now predict conditions street by street and show how far each prediction can be trusted. This project builds on two results. At Manchester, generative AI models first built for weather have been retrained on a year of hourly chemistry model output. They give UK-wide pollution maps with grid cells of 2–3 square kilometres and can run on a desktop AI computer [3]. At Cambridge, TESSERA [2] condenses a year of Sentinel-1 and Sentinel-2 satellite data into a 128-number description of every 10 m pixel of the Earth's land surface. Our recent work [1] showed that these embeddings make probabilistic weather downscaling more accurate at stations and times the model has never seen, with temperature gains generally growing as stations become sparser. This project will use Earth embeddings to add local detail and reliable estimates of uncertainty to environmental models, starting with air pollution in the UK. A first step is to add embedding-based descriptors to probabilistic and generative pollution models, trained on data from regulatory monitors and low-cost sensor networks, and to find out which pollutants benefit and why. From there, the work can go in several directions depending on the student's interests. On the methods side, this could mean models that give joint uncertainty across a whole city, or that use embeddings from different years to track change. On the decision side, the same models can be used to choose where a new sensor would reduce uncertainty most, or to judge when a forecast is confident enough to trigger a health alert. They can also be moved to a new city with few monitors. Embeddings can also find comparable places to test whether interventions, such as low-emission zones or new green space, changed local air quality. Beyond pollution, the same approach applies to other problems shaped by the land surface, such as urban heat, pollen and wildfire risk. One of our key goals is to make the results usable by people who are not modelling experts, such as council officers and clinicians. We plan to build tools that explain each prediction and how far it can be trusted. In the longer term we aim for an open workflow that any city can use to build its own detailed environmental models from public satellite data and a small number of local measurements. Student Desirable Background: This project needs strong experience in machine learning and software engineering, including building and training models on large datasets, typically from a degree in computer science, mathematics, physics or engineering. Knowledge of atmospheric science or Earth observation is not required, as the supervisors at Manchester and Cambridge will provide the domain background. By submitting an application for a University of Cambridge project, you consent to your documents being shared with the relevant supervisory team at the partner institution. If you have questions about this process aidecisionscdt@manchester.ac.uk before applying. How to apply: Please apply under the University of Manchester application portal and select 'PhD in Artificial Intelligence' ( OAA Application form ) When applying, you must include the project’s full name, your supervisor’s name, funding status, academic history, and two referees’ contact details. Applications with missing documents will not be processed. Late or incomplete submissions will not be considered, and the university is not responsible for missed deadlines. After you have applied you will be asked to upload the following supporting documents: Final Transcript and certificates of all awarded university level qualifications Interim Transcript of any university level qualifications in progress CV Supporting statement: A mandatory one- to two-page statement explaining your motivation for postgraduate research, why you chose Manchester, relevant academic or work experience, key research findings, and the techniques or skills you've gained. Applications will be put on hold if this is not provided. Contact details for two referees (Please ensure referee email addresses are official university or workplace accounts, as we may need to verify the references.) English Language Certificate (if applicable) Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. ( Equality, diversity and inclusion | The University of Manchester ) We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status. We also support applications from those returning from a career break or other roles. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is free from bias, and we are committed to ensuring fair and equal opportunities for all applicants. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs , including a part-time study option. Join us and help grow the UK’s national capability in artificial intelligence, part of the UK’s modern industrial strategy . Everyone is welcome if you are passionate about uncertainty quantification, decision making with humans in the loop and decision making for machine learning systems and their applications in Science and Engineering, and looking to develop your research and innovation skills at the doctoral level to further your career in the AI sector. Funding Notes The UKRI AI CDT in Decision Making for Complex Systems is part of the TechFirst offering stipends of £30,000 for students eligible for home fee status. The start date is September 2027. The Project will be based in University of Cambridge. References P. Sousa et al., "Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling", arXiv:2608.12271, 2026. https://arxiv.org/abs/2608.12271 Z. Feng et al., "TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis", Proc. IEEE/CVF CVPR, pp. 34818–34831, 2026. https://arxiv.org/abs/2506.20380 I. Salian, "University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK", NVIDIA Blog, 15 September 2026. https://blogs.nvidia.com/blog/uk-air-pollution-research-earth-2/