Funded PhD: AI-Driven Remote Sensing Species-Level Savannah Monitoring

Fully-funded PhD: AI-Driven Remote Sensing for Species-Level Savannah Monitoring

Savannahs cover 50% of Africa and face rapid change from woody vegetation encroachment, which threatens biodiversity, rangeland productivity, and livelihoods. Current monitoring efforts treat woody cover as a single class, masking species-specific impacts. This PhD will pioneer the first species-level monitoring framework using drone-based multispectral data fused with very-high-resolution satellite imagery (Pleiades Neo), powered by cutting-edge geospatial AI.

You will develop a scalable training pipeline to map species-level encroachment across landscapes, combining drone data with satellite products (Sentinel‑1/2, EnMAP, GEDI). The project is co‑designed with South African government agencies and supported by Airbus, providing premium satellite imagery and technical expertise. While the project includes methodological and applied components, the primary focus will be on developing and validating the scalable geospatial AI framework, with field and policy integration supported through established collaborations.

Main Aim: To develop a scalable, species-level monitoring framework for woody vegetation encroachment in African savannahs using drone and satellite data fused with advanced geospatial AI.

Specific Objectives:

• Design and implement a hierarchical training pipeline linking UAV multispectral data with very‑high‑resolution satellite imagery (Pleiades Neo).
• Conduct field campaigns in South Africa using a multispectral UAS.
• Apply self‑supervised and interpretable deep learning models to upscale species-level mapping to regional satellite products.
• Organise co‑creation workshops with local stakeholders and generate decision-ready indicators for restoration and land management, co‑designed with South African government agencies.
• Develop open-source tools (QGIS plugin and web viewer) to support operational uptake and policy integration.

• A 1st class or 2.1 degree (or equivalent) in Environmental Science, Remote Sensing, Computer Science, Surveying Engineering, or related field.
• Experience with processing and analysing remotely sensed data.
• Experience with GIS and spatial data analytical techniques.
• Experience with carrying out fieldwork in related fields (e.g., Geography, Environmental Science, Ecology).
• Experience with machine and deep learning frameworks (e.g., PyTorch, Tensor Flow) and architectures such as convolutional neural networks (CNNs) and vision transformers (ViTs).
• Experience with Google Earth Engine.

Both home and international students can apply. Only home tuition fees will be covered for the duration of the 3.5‑year award, which is £5,006 for the year 2025/26 (applied pro‑rata for part-time study, if applicable). Eligible international students will need to make up the difference in tuition fee funding (Band 3 for the year 2025/26).

The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £20,780 for the academic year 2025/26 (applied pro‑rata for part-time study, if applicable).

28 February 2026.

Please quote the reference:
Sci Eng‑ES‑2026‑27‑Savanna AI Monitoring.

Both home and international students can apply.