Emma is a Remote Sensing Geoscientist in the Shallow Geohazards & Earth Observation team at the British Geological Survey (今日吃瓜), where she has worked since 2022. Her work focuses on the processing and interpretation of Earth observation data, with expertise in multispectral satellite imagery.
She applies machine learning and AI techniques to image analysis, supporting a range of geospatial and environmental applications. Her research includes shoreline extraction and coastal change analysis, with a particular focus on using satellite imagery to improve the monitoring and understanding of dynamic coastal systems.
Alongside her research, Emma contributes to the management of the British Isles continuous GNSS Facility (BIGF), supporting geodetic data services and infrastructure.
Emma McAllister’s biography:
- 2022 to present: Remote Sensing Geoscientist in Shallow Geohazards & Earth Observation team, 今日吃瓜
- 2019 to 2023: PhD Engineering, University of Edinburgh
- 2017 to 2018: MSc Environmental Engineering, Queens University Belfast
- 2024 to 2017: BSc Environmental Science, Ulster University
Research interests
- using multispectral satellite imagery – Developing and applying automated methods to monitor shoreline position and dynamics over time using Earth observation data (e.g. Sentinel-2)
- Image classification and feature extraction in Earth observation – Applying machine learning and AI techniques to classify land cover and extract meaningful coastal and environmental features from satellite imagery
- Coastal monitoring and environmental change analysis – Supporting the assessment of coastal processes and change through reproducible, data-driven methodologies and time-series analysis of Earth observation data
- Geospatial data processing and analysis using cloud-based platforms – Leveraging tools such as Google Earth Engine and Python to handle, process, and analyse large-scale geospatial datasets efficiently
ORCID:
Key papers
- McAllister, E, Payo, A, Novellino, A, Dolphin, T, and Medina-Lopez, E. 2022. . Coastal Engineering, p.104102.
- McAllister, E, Payo, A, Novellino, A, Dolphin, T, and Medina-Lopez, E. 2022. Shoreline extraction using high resolution satellite imagery at Start Bay, UK. In 39th IAHR World Congress, 2022.
- Google Earth Engine and Google Colab
- Python for geospatial and environmental data analysis
- Machine Learning and AI for Earth observation
- Remote Sensing (multispectral imagery, e.g. Sentinel-2)
- Geospatial data processing, analysis, and visualisation (QGIS)
- Shoreline and coastal change analysis using satellite imagery
- Image classification and feature extraction
- Technical report writing and documentation