The damage resulting from natural disaster, and the resulting displacement of people is conventionally very difficult to quantify until surveys by experts on the ground are complete, an exercise which can take several months. The problem is particularly acute in some of the world’s most vulnerable communities, where poor and damaged transport and communication infrastructure may hinder the process of surveying and collating data on the ground. Understanding the spatially varying effect of cyclones and other extreme weather events on people and infrastructure is needed to inform emergency responses, to target specific support, and to quantify impacts. Recent work by the supervisory team has developed models for predicting the locations of cyclone events and their magnitude.
Aims and Objectives
This project seeks to unlock the potential for Earth Observation (EO) to be used in rapidly and reliably quantifying damage and displacement of people as a result of cyclone events. The objectives
of this project are to identify EO indicators of building and infrastructure damage, of the displacement of people, and thereby to test the reliability and resolution of those indicators, for example using historic cyclone data, with a particular focus on communities in the global south. As well as providing a tool for rapid assessment of cyclone damage, this PhD study will work towards a predictive model of cyclone damage, based on the information that can be gathered from EO data in areas of past cyclones. This will be achieved by correlating estimated damage levels by EO with wind speeds from global climate models. That correlation can then be used to estimate damage levels from future storms as predicted by climate models, incorporating the effect of climate change.
Methodology
Using established object identification algorithms developed by Airbus, the student will apply them to high resolution data in order to identify cyclone-related damage and displacement. These data will be statistically linked to cyclone event models and predictions. This problem will require spatially explicit modelling, damage and displacement models to be calibrated to the local vernacular construction, forms of infrastructure and transportation (e.g. damage to buildings constructed from natural materials, for example, such as palm-leaf roofs, are likely to present different changes in satellite images than buildings from manufactured materials do).
Research Environment
This PhD is part of the NERC and UK Space Agency funded Centre for Doctoral Training "SENSE": the Centre for Satellite Data in Environmental Science. SENSE will train 50 PhD students to tackle cross-disciplinary environmental problems by applying the latest data science techniques to satellite data. All our students will receive extensive training on satellite data and AI/Machine Learning, as well as attending a field course on drones, and residential courses hosted by the Satellite Applications Catapult (Harwell), and ESA (Rome). All students will experience extensive training on professional skills, including spending 3 months on an industry placement. See http://www.eo-cdt.org
This 3 year 9 month long NERC SENSE CDT award will provide tuition fees (£4,500 for 2021/22), tax-free stipend at the UK research council rate (£15,609 for 2021/22), and a research training and support grant to support national and international conference travel. www.eo-cdt.org/apply-now
Supervisory Team
The supervisors and supporting academics are well placed to support this work on local calibration. The proposed PhD project would work alongside an ongoing Royal-Society funded GCRF project studying cyclone damage to buildings in Madagascar. That project has gathered extensive information, from both literature review and fieldwork, on the damage from recent cyclones and the communities’ response. Airbus will provide the capture of new satellite images during the PhD over new cyclone events. They will also provide supporting training as appropriate and possibly a placement in their offices.
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