{"id":36451,"date":"2024-06-06T13:15:48","date_gmt":"2024-06-06T12:15:48","guid":{"rendered":"https:\/\/www.intelligentcio.com\/apac\/?p=36451"},"modified":"2024-06-19T12:22:06","modified_gmt":"2024-06-19T11:22:06","slug":"fighting-wildfires-from-space-in-record-time","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/apac\/2024\/06\/06\/fighting-wildfires-from-space-in-record-time\/","title":{"rendered":"Fighting wildfires from space in record time"},"content":{"rendered":"\n<p><em><strong>How AI could prevent a repeat of Australia\u2019s devastating wildfires.<\/strong><\/em><\/p>\n\n\n\n<p>Australian scientists working with cube satellites with onboard AI are now able to detect fires from space 500 times faster than traditional on-ground processing of imagery.<\/p>\n\n\n\n<p>The breakthrough means that bushfires will be detected earlier from space, even before they take hold and generate large amounts of heat, allowing on ground crews to respond more quickly and prevent loss of life and property.<\/p>\n\n\n\n<p>A project funded by the SmartSat CRC and led by the University of South Australia (UniSA) has used cutting-edge onboard AI technology to develop an energy-efficient early fire smoke detection system for South Australia\u2019s first cube satellite, Kanyini.<\/p>\n\n\n\n<p>The Kanyini mission is a collaboration between the SA Government, SmartSat CRC and industry partners to launch a 6 U CubeSat satellite into low Earth orbit to detect bushfires as well as monitor inland and coastal water quality.<\/p>\n\n\n\n<p>Equipped with a hyperspectral imager, the satellite sensor captures reflected light from Earth in different wavelengths to generate detailed surface maps for various applications, including bushfire monitoring.<\/p>\n\n\n\n<p>Lead researcher UniSA geospatial scientist Dr Stefan Peters said that, traditionally, Earth observation satellites have not had the onboard processing capabilities to analyse complex images of Earth captured from space in real-time.<\/p>\n\n\n\n<p>His team, which includes scientists from UniSA, Swinburne University of Technology and Geoscience Australia, has overcome this by building a lightweight AI model that can detect smoke within the available onboard processing, power consumption and data storage constraints of cube satellites.<\/p>\n\n\n\n<p>The AI onboard model reduced the volume of data downlinked to 16% of its original size, while consuming 69% less energy \u2013 and also detected fire smoke 500 times faster than traditional on-ground processing.<\/p>\n\n\n\n<p>\u201cSmoke is usually the first thing you can see from space before the fire gets hot and big enough for sensors to identify it, so early detection is crucial,\u201d Dr Peters said.<\/p>\n\n\n\n<p>To demonstrate the AI model, the team used simulated satellite imagery of recent Australian bushfires, using machine learning to train the model to detect smoke in an image.<\/p>\n\n\n\n<p>\u201cFor most sensor systems, only a fraction of the data collected contains critical information related to the purpose of a mission. Because the data can\u2019t be processed on board large satellites, all of it is downlinked to the ground where it is analysed, taking up a lot of space and energy. We have overcome this by training the model to differentiate smoke from cloud, which makes it much faster and more efficient.\u201d<\/p>\n\n\n\n<p>Using a past fire event as a case study, the simulated Kanyini AI onboard approach took less than 14 minutes to detect the smoke and send the data to the South Pole ground station.<\/p>\n\n\n\n<p>\u201cThis shows there are significant benefits of onboard AI compared to traditional on ground processing,\u201d Dr Peters said.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>How AI could prevent a repeat of Australia\u2019s devastating wildfires. Australian scientists working with cube satellites with onboard AI are now able to detect fires from space 500 times faster than traditional on-ground processing of imagery. The breakthrough means that bushfires will be detected earlier from space, even before they take hold and generate large [&hellip;]<\/p>\n","protected":false},"author":58,"featured_media":36452,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[23,53,44,54],"tags":[1108,6447,6448,5037,6450,6449,6446],"class_list":["post-36451","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-green","category-regional-news-newsletter","category-top-stories","category-used","tag-ai","tag-bushfires","tag-cube-satellites","tag-smartsat-crc","tag-unisa","tag-university-of-south-australia","tag-wildfires"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts\/36451","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/comments?post=36451"}],"version-history":[{"count":3,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts\/36451\/revisions"}],"predecessor-version":[{"id":36774,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/posts\/36451\/revisions\/36774"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/media\/36452"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/media?parent=36451"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/categories?post=36451"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/apac\/wp-json\/wp\/v2\/tags?post=36451"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}