概要总计 4054 字，预计今年写作天数 8 两分钟
作者 | Tate Ryan-Mosleyarchive
校对｜数日月（数据观）
编辑 | 蒲蒲
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今年 2 月以来，沙特阿拉伯海啸多发，继 2 月 6 日 7.7 级海啸后，当地天数 20 日晨（北京天数 21 日下午），沙特阿拉伯再次相继发生两次 6.4 级和 5.8 级海啸。据国际投行业务高盛公司 2 月 16 日报告分析，此次星毛给沙特阿拉伯造成的损失可能将高达 250 亿美元。
近日，麻省理工学院信息技术评论发表专文《论 AI 怎样助推大灾难积极响应》文章，指出沙特阿拉伯和叙人道项目组已经开始采用机器学习来加速确认海啸毁坏覆盖范围并制订搜救方案。
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How AI can actually be helpful in disaster response
论 AI 怎样助推大灾难积极响应
Humanitarian teams in Turkey and Syria are using machine learning to quickly scope out earthquake damage and strategize rescue efforts
沙特阿拉伯和叙人道项目组已经开始采用机器学习来加速确认海啸毁坏覆盖范围并制订搜救工作方案。
Islahiye, Turkey – Satellite imagery ( left ) and the output from xView2 ( right )MAXAR TECHNOLOGIES ( LEFT ) ; UC BERKELEY/DEFENSE INNOVATION UNIT/MICROSOFT ( RIGHT )
沙特阿拉伯东南部小城伊斯拉齐于 – 人造卫星影像（左）和 xView2 的输出（右）
麦克萨信息技术（左）；加州大学伯克利分校 / 国防创新部门 / 微软（右）
We often hear big ( and unrealistic ) promises about the potential of AI to solve the world ’ s ills, and I was skeptical when I first learned that AI might be starting to aid disaster response, including following the earthquake that has devastated Turkey and Syria.
AI 常常被赋予一些华而不实的承诺，比如解决世界难题。首次了解到 AI 或被用于此次沙特阿拉伯和叙海啸救灾时，我深表怀疑。
But one effort from the US Department of Defense does seem to be effective: xView2. Though it ’ s still in its early phases of deployment, this visual computing project has already helped with disaster logistics and on the ground rescue missions in Turkey.
然而，美国国防部在 xView2 上的努力成果似乎印证了 AI 的有效性。虽然 xView2 仍在部署早期，但这个可视化计算项目已经在沙特阿拉伯的大灾难后勤和地面搜救任务中派上用场。
An open-source project that was sponsored and developed by the Pentagon ’ s Defense Innovation Unit and Carnegie Mellon Universitys Software Engineering Institute in 2019, xView2 has collaborated with many research partners, including Microsoft and the University of California, Berkeley. It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity much faster than is possible with current methods.
xView2 是一个由美国五角大楼国防创新部门和卡内基梅隆大学软件工程研究所于 2019 年 牵头和开发的开源项目，研究合作伙伴包括微软和加州大学伯克利分校等机构。它采用机器学习算法，结合其他分销商提供的人造卫星影像来识别灾区建筑和基础设施损坏情况，并对其损坏程度进行分类，比目前已知的其他方法要快得多。
Ritwik Gupta, the principal AI scientist at the Defense Innovation Unit and a researcher at Berkeley, tells me this means the program can directly help first responders and recovery experts on the ground quickly get an assessment that can aid in finding survivors and help coordinate reconstruction efforts over time.
国防创新部门的首席 AI 科学家兼伯克利研究员 Ritwik Gupta 透露，这意味着该方案可以直接帮助现场的搜救人员和恢复专家加速获得评估，从而帮助寻找幸存者并帮助协调灾后重建工作。
In this process, Gupta often works with big international organizations like the US National Guard, the United Nations, and the World Bank. Over the past five years, xView2 has been deployed by the California National Guard and the Australian Geospatial-Intelligence Organisation in response to wildfires, and more recently during recovery efforts after flooding in Nepal, where it helped identify damage created by subsequent landslides.
在这个过程中，Gupta 经常与美国国民警卫队、联合国和世界银行等大型国际组织合作。在过去五年天数里，xView2 已经被加利福尼亚国民警卫队和澳大利亚地理空间情报组织部署采用，以应对野火。最近，在尼泊尔洪水灾后工作中，它识别到紧随其后的山体滑坡所造成的毁坏。
In Turkey, Gupta says xView2 has been used by at least two different ground teams of search and rescue personnel from the UN ’ s International Search and Rescue Advisory Group in Adiyaman, Turkey, which has been devastated by the earthquake and where residents have been frustrated by the delayed arrival of search and rescue. xView2 has also been utilized elsewhere in the disaster zone, and was able to successfully help workers on the ground be “able to find areas that were damaged that they were unaware of,” he says, noting Turkey ’ s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform in response to the earthquake.
Gupta 表示，在沙特阿拉伯的海啸搜救中，xView2 至少被两个不同的地面搜救队采用，这些地面搜救队来自沙特阿拉伯阿德亚曼的联合国国际搜救小组。该地区已经被海啸摧毁，居民因搜救人员延迟到达而感到沮丧。xView2 在灾区的其他地方也得到了应用，并且能够成功帮助当地的工作人员搜寻到他们不知道的受损区域。沙特阿拉伯的灾害和应急管理主席、世界银行、红十字国际联合会和联合国世界粮食方案署都在应对海啸时采用了该平台。
“If we can save one life, that ’ s a good use of the technology,” Gupta tells me.
” 如果我们能挽救一条生命，那这项技术就是用在点子上了 “，Gupta 说。
01
How AI can help
人工智能怎么发力？
The algorithms employ a technique similar to object recognition, called “semantic segmentation,” which evaluates each individual pixel of an image and its relationship to adjacent pixels to draw conclusions.
这些算法采用了一种类似于物体识别的技术，称为 ” 语义分割 “，它评估了每个影像的单独像素以及与相邻像素的关系，从而得出结论。
Below, you can see snapshots of how this looks on the platform, with satellite images of the damage on the left and the model ’ s assessment on the right — the darker the red, the worse the wreckage. Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes.
下面，你可以看到 xView2 平台上的快照，左边是大灾难毁坏的人造卫星影像，右边是系统模型的评估——红色越深，残骸就越严重。世界银行的灾害风险管理专家 Atishay Abbhi 表示，这种程度的评估放在以前，通常需要几周天数，而现在有了 xView2 只需要几小时或几两分钟。
Marash, Turkey: Satellite imagery ( left ) from earth imaging company Planet Labs PBC and the output from xView2 ( right ) attributed to UC Berkeley, the Defense Innovation Unit, and Microsoft.图为海啸后的沙特阿拉伯马拉什：来自地球成像公司 Planet Labs PBC 的人造卫星影像（左）和来自加州大学伯克利分校、国防创新部门和微软的 xView2 输出（右）。
This is an improvement over more traditional disaster assessment systems, in which rescue and emergency responders rely on eyewitness reports and calls to identify where help is needed quickly. In some more recent cases, fixed-wing aircrafts like drones have flown over disaster areas with cameras and sensors to provide data reviewed by humans, but this can still take days, if not longer. The typical response is further slowed by the fact that different responding organizations often have their own siloed data catalogues, making it challenging to create a standardized, shared picture of which areas need help. xView2 can create a shared map of the affected area in minutes, which helps organizations coordinate and prioritize responses — saving time and lives.
这是对传统灾害评估系统的转型升级，在这个系统中，搜救和应急积极响应人员依靠目击者的报告和电话来迅速确认哪里需要帮助。在最近的一些案例中，像无人机这样的固定翼飞机带着摄像机和传感器在灾区上空飞行，提供由人类审查的数据，但这仍然需要几天天数，甚至更久。由于不同的救灾组织往往有自己的独立数据目录，使得创建一个标准化的、可共享的关待搜救地区图片变得很有挑战性，这有助于组织协调积极响应并确认积极响应的优先级，从而节省天数挽救更多生命。
02
The hurdles
阻碍
This technology, of course, is far from a cure-all for disaster response.There are several big challenges to xView2 that currently consume much of Gupta ’ s research attention.
当然，这项技术远非大灾难积极响应的灵丹妙药。目前，xView2 面临着几项重大挑战，这些挑战消耗了 Gupta 的大部分研究注意力。
First and most important is how reliant the model is on satellite imagery, which delivers clear photos only during the day, when there is no cloud cover, and when a satellite is overhead. The first usable images out of Turkey didn ’ t come until February 9, three days after the first quake. And there are far fewer satellite images taken in remote and less economically developed areas — just across the border in Syria, for example. To address this, Gupta is researching new imaging techniques like synthetic aperture radar, which creates images using microwave pulses rather than light waves.
首先，最重要的是该模型对人造卫星影像的依赖程度，人造卫星影像只在白天没有云层和人造卫星覆盖的时候提供清晰的照片。沙特阿拉伯第一批可用的影像直到 2 月 9 日才出现，即第一次海啸发生后三天。而且，在偏远和经济欠发达地区拍摄的人造卫星影像要少得多——例如，叙边境。为了解决这个问题，Gupta 已经开始研究新的成像技术，如合成孔径雷达，它采用微波脉冲而非光波来创建影像。
Second, while the xView2 model is up to 85 or 90% accurate in its precise evaluation of damage and severity, it also can ’ t really spot damage on the sides of buildings, since satellite images have an aerial perspective.
其次，虽然 xView2 模型在精确评估损坏和严重程度方面的准确率高达 85% 或 90%，但它也无法真正发现建筑物侧面的损坏程度，因为人造卫星影像具有航空视角。
Lastly, Gupta says getting on-the-ground organizations to use and trust an AI solution has been difficult. “First responders are very traditional,” he says. “When you start telling them about this fancy AI model, which isn ’ t even on the ground and it ’ s looking at pixels from like 120 miles in space, they ’ re not gonna trust it whatsoever.”
Gupta 表示，让搜救实地组织采用和信任 AI 解决方案一直很困难。” 搜救人员非常传统，” 他说。” 当你告诉他们这个奇特的 AI 模型甚至不在地面上，而是从 120 英里的太空中来观察地面像素，他们无论怎样都不会相信它。”
03
What ’ s next
路在何方
xView2 assists with multiple stages of disaster response, from immediately mapping out damaged areas to evaluating where safe temporary shelter sites could go to scoping longer-term reconstruction. Abbhi, for one, says he hopes xView2 “will be really important in our arsenal of damage assessment tools” at the World Bank moving forward.
xView2 可以协助多个阶段的救灾工作，从立即绘制受损地区的地图到评估安全的临时庇护所的位置，再到确认长期的灾后重建覆盖范围。Abbhi 表示，他希望 xView2 在损害评估工具库中发挥重要作用，引领世界银行向前发展。
Since the code is open source and the program is free, anyone could use it. And Gupta intends to keep it that way. “When companies come in and start saying, We could commercialize this, I hate that,” he says. “This should be a public service that ’ s operated for the good of everyone.” Gupta is working on a web app so any user can run assessments; currently, organizations reach out to xView2 researchers for the analysis.
由于代码是开源的，程序是免费的，任何人都可以采用它，而且 Gupta 打算保持这种方式。他说：” 当公司进来并开始说，我们可以把这个商业化，我讨厌这样。xView2 应该是一项公共服务，为了每个人的利益而运作。” 目前，Gupta 已经开始开发一个网络应用，这样任何用户都可以运行评估功能，而各组织也在向 xView2 的研究人员提供分析服务。
Rather than writing off or over-hyping the role that emerging technologies can play in big problems, Gupta says, researchers should focus on the types of AI that can make the biggest humanitarian impact. “How do we shift the focus of AI as a field to these immensely hard problems?” he asks. ” [ These are ] , in my opinion, much harder than — for example — generating new text or new images.”
Gupta 认为，研究人员不应将新兴技术在大问题上发挥的作用一笔勾销或过度夸大，而应将重点放在能够产生最大人道影响的人工智能应用上。那么，怎样将人工智能的重点作为一个领域转移到这些世界级难题上呢？” 在我看来，这些比生成新文本或新影像要难得多。”
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