Bayes Centre

Menu

Space Intelligence Wins Shell's GameChange Competition

Space Intelligence look to turn social-media photos into nature-conservation data, with new funding from Shell’s GameChanger programme.

Space Intelligence, based in Edinburgh, has secured a $150,000 grant from Shell’s GameChanger programme to develop a proof-of-concept system that combines social-media images of naturewithdata from earth-orbiting satellites,to produce detailed landcover mapsthat might ultimately be used to tackle climate change. Thecompanywill first apply its system in Brazilbut hopesto roll it outacross the globe. The programme is named Rebrota, meaning regrowth in Portuguese.

 

Space Intelligence CEO Dr Murray Collins said:

“We are extremely excited to have won this highly competitive funding, and to work with the GameChanger team to help mitigate the negative effects of climate change. In the long term we plan to apply our innovative machine-learning approaches more widely aswe work towards our company vision of a world with massively upscaled forest restoration and conservation activity.”

 

Jeroen Smith of the Shell Game Changer team said:

“Shell invests in and manages a range of nature-based projects that help offset or store emitted carbon. These include the protection and restoration of ecosystems like forests, grasslands and wetlands.Monitoring the progress of these projects is challenging, as they are often in hard-to-reach locations. The Space Intelligence system shows how these nature-based solutions could be monitored remotely with a bold proof-of-concept testonthe diverse vegetation of the Brazilian Cerrado”.

 

Brazil is known for its rainforest, but it also hosts a vast savanna known as the Cerrado, which coversa similar area to the size of France, Spain, Germany, Italy and the UK put together. Considered to be as important environmentally as the Amazon basin, the Cerrado is home to 5% of the world’s animals and plantsyet is much more threatened than the rainforest. Two thirds of its vegetation has been destroyed since 1960, when the nation's capital Brasilia was built in the region with a road network that opened it up to monoculture agriculture and pasture.The time and cost involved in methodically surveying on foot a massive wilderness the size of Western Europe has historically made it impossible to draw up-to-date, localised maps which show what is happening on the ground.

Space Intelligence's system intends to rectify this. It is the winning proposal out of the 73 that were submitted by companies worldwide in response to GameChanger's public call for solutions that transform earth-observation data into actionable insights that could help address climate-change challenges.Using machine-learning techniques, the system"trains" a computerto derive meaning out of the plethora of geolocated images thatpeople trekking in the Cerrado upload onto social-media platforms, such as Twitter, Instagram, Flickr and Google Maps. The resulting inferred knowledge is thenmatched with satellite data to generate environmental mapsof the entire region.

Saptarshi Das, Data Science R&D Manager at Shell, said:

“Space Intelligence's concept of fusing satellite data and social-media data is unique.It isinan incredible application of artificial intelligence to address climate-changechallenges.”

 

As of 2019, 3.2 billion images were being uploaded onto social media around the world every day. Even if only one in 10,000 images contain relevant content, thatstill comes to320,000 useful images a day, or 117 million useful images annually. Rather than just sitting there, they could be analysed to tell us more about the state of our world. Whilst their sheer quantity is too much forhuman specialists to cope with, machine learning will automate the analysis, turning a never-endingtorrentof visual data into easy-to-understand landcover maps offering unprecedented detail and scalability.

Space Intelligence CTO Professor Ed Mitchard said:

“As far as we are aware, nobody has ever used a machine-learning system on public photos to create training data for earth-observation image classification. We think the time is right to use the deluge of social-media images that exists in regions like this. These data are shared by people who care about these ecosystems, so we’re delighted to be able to use them to help with conservation. Machine learning and cloud computing systems have advanced to the stage that analysing all these datais feasible. We can't wait to get started, and are hugely grateful to Shell for the support.”