The following questions are aimed to capture a snapshot of an individual student within one the CDT cohorts. During proposal development, hypothetical students (with real Southampton supervisors, and hypothetical industrial ones) are used to help shape the training programme and give light to how the CDT would be experienced from the perspective of the student. If successfully funded, the same questions will be reused to help us tell the stories of our actual students.

Describe the student’s previous academic and/or professional background

XXX undertook a Batchelor’s degree in Geography and an MSc in Applied GIS and Remote sensing both at Southampton. His dissertation was contributing to some aspect of a research project funded by ESA to look at time series of satellite data for vegetation condition monitoring.

What is the student’s motivation for joining the Geospatial AI CDT at Southampton?

The student was really interested in using data from a recently launched satellite sensors from ESA and develop new vegetation monitoring products. They were also interested in collecting different ground dada to validate those products. The CDT offered the opportunity to work closely with ESA and an opportunity to spend 6 months internship in one of their locations. Also access to local company to develop innovating satellite based monitoring product was attractive. Further opportunity to strengthen the AI and computing skills, which would not be possible in a standard’ Geography based PhD.

Who are the student’s supervisors? What is their research topic/area?

XXX was supervised by Professor Jadu Dash and Dr Booker Ogutu, both in Geography and environmental Science. Both supervisors have research interest in remote sensing of vegetation. Jadu has a strong relationship with ESA which provided further networking opportunity for the student.

How has the Geospatial AI CDT provided a technical training programme that is tailored to the student’s needs and their research topic/area?

Student had considerable knowledge about the ecosystem and environment from their undergraduate and master’s degree, with a very good experience of using GIs and remote sensing tools. But the CDT provided foundational training in AI and pattern/image recognition, data handling through python which helped to develop new algorithm to extract information from Satellite data. The studnet was able to apply number of machine learning approaches to derive vegetation monitoring variables from satellite data. Moreover, the opportunity to learn and utilise cloud based data processing helped with application of those techniques to a much larger global dataset.

How has the Geospatial AI CDT provided training required to allow the student to become a future leader in the field? Are there any particular transferrable or generic skills that this student has mastered?

The programming and data analytics skills along with the knowledge of AI is crucial for a researcher in satellite remote application. With increasing amount of data availability, use of AI is becoming one of the most sought after technology. Training on research project development and research management was helpful for the student to submit a fellowship proposal after their PhD. Training on generic skills such as presentation and group work was useful for developing their network. Activities such as Hackathon and internship was used as a pilot to test and develop idea for fellowship research proposal.

In what ways has this student excelled in working in a group within their cohort during the training programme? How has the training programme facilitated this?

We have quite few students with background in geospatial data application, they provide complementary skills to the other group of students with theoretical and mathematical knowledge of AI. Seminar programmes were very helpful to share idea and in some cases that led on to joint mini projects and partnership for Hackathon project. Two groups submitted proposal to Copernicus Masters programme (https://copernicus-masters.com/), an international competition for innovative Earth Observation solutions. These group activities provided opportunity to develop nontechnical communication skills.

Has the student generated any intellectual property during the programme? (Briefly) what is it? Do the findings impact the public, private or third/voluntary sectors, and elsewhere?

Mostly papers. However, for some of the ideas either in hackathon or Copernicus masters further commercial opportunity could have been explored.

Which CDT partner(s) are (co-)sponsoring the studentship award?

ESA provided in-kind support for staff time and organised the internship.

If the student has undertaken the internship who was it with, and what did they do?

XXX spent some time working with ESA at one of their project locations. The work looked at how operational satellite products are being generated (both hardware and software) , interaction and feedback from users and solution to any data specific problems.

If the student has finished where are they going next?

The student secured an ESA living planet fellowship and has a lectureship at a UK University.

How have Southampton’s unique facilities and broad range of Geospatial and AI expertise benefitted the student?

Existing partnership with ESA helped to formulate the project and access to range of expertise across the university ensure the smooth running of the project. In terms of facilities, the computing infrastructure provided by the CDT through the host School and wider University was critical to facilitating the research.