Chilmark plays host to international search and rescue drone experiment
It could play a vital role in future operations
The UK's first post-disaster search and rescue experiment for drone applications has been going on near Salisbury.
Chilmark has been playing host to the operation, which includes researchers from the University of Portsmouth and the UN World Food Programme.
Using search-drones in emergency situations such as floods, droughts, storms or earthquakes, enables helicopters and planes to be re-deployed for rescuing people and delivering essential aid.
Drones also have low operating costs and can be deployed quickly, even in difficult weather conditions, but little is known about the most effective ways to use them over large areas.
So different types of drone, alongside different types of camera are being tested to see what would be most effective in emergency situations.
In November, University of Portsmouth researchers played a key role in a flood disaster simulation based around a lake in Mozambique.
Using drones they searched for volunteers who needed rescuing, as well as locating local hazards, such as crocodiles.
The tests started last Thursday (9th June) and continue until tomorrow (Tuesday 14th).
It's being led by Professor Richard Teeuw, with around 20 drone pilots from Africa, Europe, the UK, and Canada.
Professor Teeuw said:
“The University of Portsmouth has been running training courses in Crisis and Disaster Management for over 10 years, with annual emergency response simulation events.
“It also hosts a centre of excellence for space technology applications, including drones, so this research with the United Nations, into ways of using drones to save lives during disasters, is an effective partnership.”
WFP Drones Team Project Manager, Elizabeth Bourke, added:
“In the first phase in Mozambique we already made some valuable findings on how to most effectively use drones in a wide area search to find the location of people needing rescue. In this second phase, we are focusing on the use of machine learning to even more rapidly detect people in a large-scale emergency.
“Through our collaboration with the University of Portsmouth we're working to develop these tools as well as a set of best practice and Standard Operating Procedure documents to guide future emergency teams to most effectively identify the location of people needing rescue. Doing that will mean we can use the rescue helicopters and boats more efficiently and ultimately save more lives.”