The focus of the Environment and Agriculture Theme is to apply advanced data collection and analysis methods to address environmental grand challenges, covering precision agriculture to various environmental applications. We have cross-disciplinary expertise in developing machine learning and AI, spatial and space-time statistics, and data fusion techniques for geospatial science and remote sensing.
Environmental Modelling
Theme Leaders
Professor Michael James
Professor in VolcanologyCentre of Excellence in Environmental Data Science, DSI - Environment, Earth Science, Lancaster Intelligent, Robotic and Autonomous Systems Centre, LIRA - Environmental Modelling, Understanding a changing planet
Members
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Publications
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Projects
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Control and Navigation of Cooperative Unmanned Aerial Systems for Characterisation of Environmental Processes
01/02/2022 → 01/08/2025
Research
Mapping geographically co-occurrent cancers in the Morecambe Bay area for designing targeted community-based interventions.
01/02/2022 → 01/12/2024
Research
Design-Thinking in Action: Challenges and management for innovation success
01/11/2020 → 01/11/2022
Research
Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework
31/01/2020 → 16/09/2024
Research
Parametric modelling of complex erosion damage patterns of wind turbine blade leading edges
01/01/2020 → 31/03/2021
Research
NIHR Applied Research Collaboration North West Coast
01/10/2019 → 31/03/2026
Research