{"id":88944,"date":"2022-08-29T18:00:00","date_gmt":"2022-08-29T18:00:00","guid":{"rendered":"https:\/\/www.bgs.ac.uk\/?p=88944"},"modified":"2024-02-27T12:02:01","modified_gmt":"2024-02-27T12:02:01","slug":"citizen-science-become-part-of-a-real-time-global-landslide-detector","status":"publish","type":"post","link":"https:\/\/www.bgs.ac.uk\/news\/citizen-science-become-part-of-a-real-time-global-landslide-detector\/","title":{"rendered":"Citizen science: become part of a real-time Global Landslide Detector"},"content":{"rendered":"\n
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Social media is a valuable tool. Society is now sharing a constant stream of real-time information across a wide variety of digital platforms. Alongside all the pictures of pets, family members and delicious dinners, social media channels are also used when disaster strikes. In this respect, social media has the power to harness important data that can have a huge impact for the global good. Take, for instance, the ability to learn about natural disasters across the world, as they unfold in real time.<\/p>\n\n\n\n

BGS has been working with earthquake and social media specialists at the European-Mediterranean Seismological Centre<\/a> and computer scientists at the Qatar Computing Research Institute<\/a> to build the Global Landslide Detector, a tool that uses machine learning to recognise landslides in photographs shared on social media.<\/p>\n\n\n\n

When a landslide happens, the damage caused is usually unknown beyond the locality until news reporters can attend the scene or once satellites have been able to collect images, and their responding communities have processed the data. This is called \u2018data latency\u2019 and can take some time \u2013 at best a few hours; at worst, several days.<\/p>\n\n\n\n

We realised we could help improve this situation by speeding up the timescale in which landslide data becomes available on a global scale.<\/p>\n\n\n\n

Why is social media useful for landslides research?<\/h2>\n\n\n\n

Social media information is imperfect; there is an awful lot of content being produced very quickly and it is constantly being added to. While this can seem a little overwhelming, if you have a way of untangling valuable updates from the social noise, what you will end up with is data in large quantities, in real time, covering geographical areas much larger than any conventional ground sensors can detect. These \u2018social sensors\u2019 allow access to a rich source of human information such as text, videos, photographs, timestamps and locations. This can provide us with disaster information very quickly.<\/p>\n\n\n

\"A
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A typical tweet from the @BGSLandslides team. BGS \u00a9 UKRI.<\/p>\n<\/div>\n\t\t\t\t\t\n\t\t\t\t<\/figcaption><\/figure>\n\n\n

The Global Landslide Detector<\/a> is a publicly available web service that extracts, in real time, photographs of landslides published on social media. This is something we hope will be welcomed by various sectors, including disaster risk reduction managers, first responders and landslide database researchers.<\/p>\n\n\n\n

The detector currently monitors Twitter only, although there is scope for expansion in the future. It extracts tweets that contain a photograph in association with particular keywords. The keywords we\u2019ve inputted are currently available in 31 different languages, which means that translation shouldn\u2019t be a barrier to using the tool. Using machine learning, the tool then analyses the photographs automatically, to decide whether they contain a landslide or not. The data from this process is then organised by location and markers are placed on a map.<\/p>\n\n\n

\"Diagram
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Diagram showing how the Global Landslide Detector extracts relevant information from Twitter and sorts photographs into landslides and not landslides. BGS \u00a9 UKRI.<\/p>\n<\/div>\n\t\t\t\t\t\n\t\t\t\t<\/figcaption><\/figure>\n\n\n

How you can help<\/h2>\n\n\n\n

Currently we are trialling the first version of the tool<\/a> on the website. We are asking for feedback on several elements from the scientific landslide community, as well as anyone else with an active interest, including:<\/p>\n\n\n\n