Towards Global Monitoring of Glaciers with Deep Learning


Glaciers are a key indicator of climate change and have shrunk rapidly across the world in recent decades. Quantifying changes in glacier dynamics is not only vital for monitoring the impacts of climate change, but also for assessing their potential to generate hazards, such as glacial lake outburst floods, and to sustain water supplies during dry periods. Thus, mapping global glaciers changes will provide vital information for estimates of global sea level rise and regional to local scale impacts of glacier shrinkage. This project aims to deploy Deep Learning models to comprehensively scan Sentinel-2 and landsat-7,8 images (30 year timespan) in order to build a global picture of glacier dynamics in the last 3 decades.

Click on an image to expand

Image Captions

Hellheim Glacier


This project will combine Google Earth Engine and Deep Learning in order to develop an automated method of glacier inventory. Google Earth Engine (GEE) delivers an unprecedented access to the data archives for the Landsat and Sentinel programs. Using the GEE API it becomes possible to programmatically access satellite tiles for the entire world and for any data available in the archive. The resulting volume of available data will require a combination of automation and reliability that can only be delivered by Deep Learning (DL). This project will use a 2-part DL workflow: 1) the project will develop a DL object localisation algorithm that is capable of identifying the presence of glaciers in a given satellite tile; 2) the project will refine an existing DL workflow, SEE_ICE (available on GitHub), in order to perform semantic classification of detected glaciated landscapes. Training and validation data for the DL architectures will be derived from the Randolph Glacier Inventory databases combined with extensive manual digitisation. The code will be developed in Python using the Tensorflow DL library.

Project Timeline

Year 1

Assembly of training and validation data
Preliminary work on the object localisation network architecture
Refinement of the semantic classification network architecture.
Integration of the GEE API in to deep architecture pipelines.
Python and machine learning training delivered by the Durham University Advanced Research Computing facility.

Year 2

Student further training at the Karthaus Glaciology Summer school.
Finalisation and training of the DL models. Computing support for resource intensive model training will be provided by the Durham University Advanced Research Computing facility
Global scale validation of the models

Year 3

Global scale deployment of DL workflow using Google Colab cloud computing.
Compilation and dissemination
EGU conference in the spring.

Year 3.5

Writing and AGU conference in December

& Skills

The student will benefit from machine learning training at Durham University and a high-level glaciology summer school event.

References & further reading

Pfeffer, W.T., Arendt, A.A., Bliss, A., Bolch, T., Cogley, J.G., Gardner, A.S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E.S., Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B.H., Rich, J., Sharp, M.J., Consortium, T.R., 2014. The Randolph Glacier Inventory: a globally complete inventory of glaciers. J. Glaciol. 60, 537–552.

The Randolf Glacier Inventory website:

Google Earth Engine:
The SEE_ICE Github project :

Further Information

Please contact for any enquiries.

Apply Now