Improved Management of Climate & Environment Risks through the automated detection of landslides
Send indication of interest (short email with 2 page CV) to bourkem4 [at] tcd.ie ASAP
Dr M. Bourke, Trinity College, Dublin
Dr F. Pilla, University College, Dublin
Mass movement inventories are required for the assessment of susceptibility and for understanding risk. However inventories are, by their nature, a time- and resource-intensive activity. Recently, methodologies for the automatic detection of landforms have made significant advances that may enable a more accurate and efficient approach to be taken. Coastal landslides have not received sufficient treatment due largely to the inaccessibility of most coastal environments. This is a concern as future climate change predictions for Ireland suggest that there may be an increase in the frequency of coastal landslides as both rainfall intensities and coastal storms are set to increase.
We propose to explore the use of
- machine learning algorithms,
- deep learning algorithms for computer vision and
- drone technology
to enable more accurate mapping and therefore inventory building capability of mass movements. This approach will also result in a more time-effective framework to produce coastal landslide inventories.
Candidate currently without a permanent post, < 3 yrs since PhD
- Academic excellence and a proven track record in accordance with the stage of the candidate’s career.
- Demonstrated expertise relevant to the research proposed.
- Personal commitment.
- Potential for independence of research.
- Internationally mobile candidates
- Communication skills demonstrated through interactions with a non-academic audience (interviews with media, presentations in conferences with a broad public etc.).
Further funding information here: https://www.axa-research.org/en/page/post-doctoral-fellowships