Machine learning in disaster management and environment protection

Situation awareness is key to mitigating and responding to disasters to help save life and protect the natural environment.  An abundance of data is available from satellites to tweets, from UAVs to in-situ sensors, from expert knowledge to mathematical hazard models and making sense of all this in a timely manner and accurately is critical to effective disaster management.  We are developing novel machine learning approaches to data fusion, that combine all this data together, removing unreliable data, identifying informative sources to generate maps, reports, of where help is needed, what help is needed and where help is available.  This information is passed to first responders, NGOs (e.g. insurance providers), National Disaster Management Agencies, farmers and others affected by the disaster to help formulate policy and plan response effectively, efficiently and accurately.

rrpicWe are working closely with both industry and end-users, folding in UK expertise and in-country partners to co-design and deliver effective situation awareness technology in the form of dashboards, apps etc.  We are developing technology that exploits recent advances in crowdsourcing, Bayesian classifier combination, deep learning, heterogeneous data fusion and scalable inference to provide products to be used for disaster policy, planning and response.   We are, for example, working with flood hazard modellers to improve the accuracy of their models in Malaysia.  We are also developing foundational hybrid Bayesian deep learning and classifier combination approaches to exploit human-agent collectives to interpret satellite imagery for applications as far ranging as illegal logging and micro-insurance.  This work is currently being developed for exploitation in Malaysia, Ethiopia and Kenya through UK Space Agency International Partnership Programme projects with Airbus and the Satellite Applications Catapult.  Our previous work, which mapped settlements using satellite imagery interpreted through crowdsourcing following the Nepal Earthquake in 2015 was used by disaster responders to target the delivery of aid and was recently featured at the World Economic Forum in Davos.

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Part of The Alan Turing Institute-Lloyd’s Register Foundation Programme for Data-Centric Engineering.

Funded by:

Lloyds R