AI and satellites help aid workers respond to Myanmar earthquake damage
Just after sunrise on Saturday, a satellite set its long-range camera on the city of Mandalay in Myanmar, not far from the epicenter of Friday's 7.7 magnitude earthquake that devastated the Southeast Asian county's second-largest city.
The mission was to capture images that, combined with artificial intelligence technology, could help relief organizations quickly assess how many buildings had collapsed or were heavily damaged and where helpers most needed to go.
At first, the high-tech computer vision approach wasn't working.
'The biggest challenge in this particular case was the clouds,' said Microsoft's chief data scientist, Juan Lavista Ferres. 'There's no way to see through clouds with this technology.'
The clouds eventually moved and it took a few more hours for another satellite from San Francisco-based Planet Labs to capture the aerial pictures and send them to Microsoft's philanthropic AI for Good Lab. By then it was already about 11 p.m. Friday at Microsoft headquarters in Redmond, Washington. A group of Microsoft workers was ready and waiting for the data.
The AI for Good lab has done this kind of AI-assisted damage assessment before, tracking the flooding that devastated Libya in 2023 or this year's wildfires in Los Angeles. But rather than rely on a standard AI computer vision model that could run any visual data, they had to build a customized version specific to Mandalay.
'The Earth is too different, the natural disasters are too different and the imagery we get from satellites is just too different to work in every situation,' Lavista Ferres said. For instance, he said, while fires spread in fairly predictable ways, 'an earthquake touches the whole city' and it can be harder to know in the immediate aftermath where help is needed.
Once the AI analysis was complete, it showed 515 buildings in Mandalay with 80% to 100% damage and another 1,524 with between 20% and 80% damage. That showed the widespread gravity of the disaster, but, just as important, it helps pinpoint specific locations of damage.
'This is critical information for teams on the ground,' Lavista Ferres said.
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