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緬甸大地震,,人工智能助力災(zāi)后救援

美聯(lián)社
2025-04-03

微軟公益性的AI for Good Lab開展了人工智能輔助災(zāi)害評估工作。

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上周五,,緬甸第二大城市遭遇了一場7.7級地震,,致使城市遭受重創(chuàng)。圖片來源:Chen Yongnuo/China News Service/VCG via Getty Images

上周六,,日出后不久,,一顆衛(wèi)星將遠程攝像機對準了緬甸的曼德勒市,該市距離周五那場7.7級地震的震中不遠,,那場地震給緬甸的第二大城市帶來了嚴重破壞,。

此次衛(wèi)星的任務(wù)是拍攝圖像,這些圖像將與人工智能技術(shù)相結(jié)合,,助力救援組織快速評估出建筑物倒塌或嚴重受損的數(shù)量,,進而精準定位救援人員最迫切需要前往的區(qū)域。

起初,,這種高科技的計算機視覺方法并未奏效,。

微軟首席數(shù)據(jù)科學(xué)家胡安·拉維斯塔·費雷斯(Juan Lavista Ferres)表示:“在這次特定事件中,我們面臨的最大障礙來自云層,。這項技術(shù)無法穿透云層,。”

云層最終散去,,總部位于舊金山的行星實驗室(Planet Labs)調(diào)動另一顆衛(wèi)星,,耗費數(shù)小時完成了航拍照片的拍攝工作,并將其傳輸給微軟公益性的AI for Good Lab,。彼時,,華盛頓州雷德蒙德的微軟總部時間已接近周五晚上11點,一群微軟員工已準備就緒,,等待接收數(shù)據(jù),。

AI for Good Lab此前已開展過人工智能輔助災(zāi)害評估工作,比如追蹤2023年利比亞的特大洪災(zāi),,以及今年洛杉磯的野火情況,。不過,此次面對緬甸曼德勒市的情況,,他們沒有依賴那種能夠處理任何視覺數(shù)據(jù)的通用人工智能計算機視覺模型,,而是必須針對曼德勒市的獨特狀況構(gòu)建定制化模型。

拉維斯塔·費雷斯說:“地球各個區(qū)域的地理環(huán)境差異太大,,自然災(zāi)害也各不相同,,我們從衛(wèi)星獲取的圖像在不同情形下同樣存在顯著差異,這使得統(tǒng)一的模型難以普適,?!彼e例說明,火災(zāi)的蔓延方式相對可預(yù)測,,但“地震會波及整個城市”,,在震后初期很難迅速確定哪些區(qū)域迫切需要援助。

人工智能分析的結(jié)果顯示,,曼德勒市有515棟建筑物的受損程度達到80%至100%,,另有1524座建筑物的受損程度在20%至80%之間。這一數(shù)據(jù)不僅凸顯了這場災(zāi)難波及范圍之廣,、破壞程度之重,,同樣重要的是,它還有助于確定具體的受損地點,。

拉維斯塔·費雷斯說:“這對地面救援團隊而言是至關(guān)重要的信息,。”

微軟提醒稱,,該報告“應(yīng)作為初步指導(dǎo),,要全面了解情況還需進行實地核查”。但與此同時,,這家科技公司已將分析結(jié)果分享給了紅十字會等救援組織,。

行星實驗室表示,,自上周五地震發(fā)生以來,其部署在地球軌道上的15顆衛(wèi)星已對緬甸和泰國大約12個關(guān)鍵地點進行了拍攝,。(財富中文網(wǎng))

譯者:中慧言-王芳

上周六,,日出后不久,一顆衛(wèi)星將遠程攝像機對準了緬甸的曼德勒市,,該市距離周五那場7.7級地震的震中不遠,,那場地震給緬甸的第二大城市帶來了嚴重破壞。

此次衛(wèi)星的任務(wù)是拍攝圖像,,這些圖像將與人工智能技術(shù)相結(jié)合,,助力救援組織快速評估出建筑物倒塌或嚴重受損的數(shù)量,進而精準定位救援人員最迫切需要前往的區(qū)域,。

起初,,這種高科技的計算機視覺方法并未奏效。

微軟首席數(shù)據(jù)科學(xué)家胡安·拉維斯塔·費雷斯(Juan Lavista Ferres)表示:“在這次特定事件中,,我們面臨的最大障礙來自云層,。這項技術(shù)無法穿透云層?!?/p>

云層最終散去,,總部位于舊金山的行星實驗室(Planet Labs)調(diào)動另一顆衛(wèi)星,耗費數(shù)小時完成了航拍照片的拍攝工作,,并將其傳輸給微軟公益性的AI for Good Lab,。彼時,華盛頓州雷德蒙德的微軟總部時間已接近周五晚上11點,,一群微軟員工已準備就緒,,等待接收數(shù)據(jù)。

AI for Good Lab此前已開展過人工智能輔助災(zāi)害評估工作,,比如追蹤2023年利比亞的特大洪災(zāi),,以及今年洛杉磯的野火情況。不過,,此次面對緬甸曼德勒市的情況,,他們沒有依賴那種能夠處理任何視覺數(shù)據(jù)的通用人工智能計算機視覺模型,而是必須針對曼德勒市的獨特狀況構(gòu)建定制化模型,。

拉維斯塔·費雷斯說:“地球各個區(qū)域的地理環(huán)境差異太大,,自然災(zāi)害也各不相同,我們從衛(wèi)星獲取的圖像在不同情形下同樣存在顯著差異,,這使得統(tǒng)一的模型難以普適,。”他舉例說明,,火災(zāi)的蔓延方式相對可預(yù)測,,但“地震會波及整個城市”,,在震后初期很難迅速確定哪些區(qū)域迫切需要援助。

人工智能分析的結(jié)果顯示,,曼德勒市有515棟建筑物的受損程度達到80%至100%,,另有1524座建筑物的受損程度在20%至80%之間。這一數(shù)據(jù)不僅凸顯了這場災(zāi)難波及范圍之廣,、破壞程度之重,,同樣重要的是,,它還有助于確定具體的受損地點,。

拉維斯塔·費雷斯說:“這對地面救援團隊而言是至關(guān)重要的信息?!?

微軟提醒稱,,該報告“應(yīng)作為初步指導(dǎo),要全面了解情況還需進行實地核查”,。但與此同時,,這家科技公司已將分析結(jié)果分享給了紅十字會等救援組織。

行星實驗室表示,,自上周五地震發(fā)生以來,,其部署在地球軌道上的15顆衛(wèi)星已對緬甸和泰國大約12個關(guān)鍵地點進行了拍攝。(財富中文網(wǎng))

譯者:中慧言-王芳

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 country’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 Libya’s catastrophic flooding 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.

Microsoft cautioned that it “should serve as a preliminary guide and will require on-the-ground verification for a complete understanding.” But in the meantime, the tech company has shared the analysis with aid groups such as the Red Cross.

Planet Labs says its satellites — it has 15 of them orbiting the Earth — have now photographed roughly a dozen locations in Myanmar and Thailand since Friday’s quake.

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