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Next generation landscape mapping: machine learning and big data methods for exploiting earth observation data


Project Description

This is an exciting opportunity to explore machine learning and big data methods, in combination with earth observation data, to extract information on land cover and habitat condition. It will use a range of remote sensing data sets, including LIDAR, aerial photography and high resolution satellite data.

Land cover is a key environmental variable and is typically classified from satellite data, which has a relatively coarse spatial scale (> 20m). However, land cover change and key habitats often occur at spatial scales below this. Large data sets of aerial photography, LiDAR and high resolution satellite data are increasingly being collected, but efficiently extracting useful information from them requires development of new methods.

Over recent years the deep learning and machine learning communities have made rapid progress in developing methods for classifying aerial photography, however, this work has typically focussed on the requirements of the military and disaster response.

This project will use machine learning methods to classify remote sensing data for environmental purposes, as well as for quantifying habitat condition. It will explore novel applications including the combined use of LiDAR and aerial photography, and different types, combinations and scales of earth observation data. The PhD will be structured around a series of case studies exploring different applications and spatial scales and will be underpinned by field data sets held by CEH. The aim of the project is to demonstrate the potential of machine learning methods and to gain an understanding of when (and how) they are most usefully applied.

Successful development of these methods will transform our ability to monitor the natural environment remotely by revolutionising the type and speed of products we produce.

The student will be supervised by Dr Clare Rowland and Professor Alan Blackburn. The student will be based at CEH Lancaster and the PhD will be awarded by the University of Lancaster.

This PhD is interdisciplinary in nature and as such would such would suit applicants from a wide range of numerate, scientific backgrounds, including (but not limited to) candidates with degrees in Environmental Science, Geography, Ecology, Biological Sciences, Physics or Engineering. MSc’s in a relevant subject such as Remote Sensing, Environmental Modelling or Data Science would be an advantage, but an MSc, whilst desirable, is not essential. Experience with programming is desirable.

Please apply via the ENVISION portal: http://www.lancaster.ac.uk/fas/centres/envision-dtp/portal/apply.php

Funding Notes

This project is one of a number of proposed topics that are in competition for funding from the NERC ENVISION Doctoral Training Partnership View Website.

Full studentships (fees and stipend) are only available to UK nationals and other EU nationals that have resided in the UK for three years prior to commencing the studentship. If you are a citizen of an EU member state you will eligible for a fees-only award, and must be able to show at interview that you can support yourself for the duration of the studentship at the UKRI level.

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