Four years ago, here at TED, I announced Planet's Mission 1:
四年前，在这个TED会场，我宣布了《星球任务一号》：
to launch a fleet of satellites that would image the entire Earth, every day, and to democratize access to it.
我们发射了一支卫星舰队，用来拍摄地球每天的影像，并让大众能取得这些信息。
The problem we were trying to solve was simple.
我们要解决的问题很简单。
Satellite imagery you find online is old, typically years old,
你在网络上看到的卫星影像是旧的，通常是几年前的，
yet human activity was happening on days and weeks and months, and you can't fix what you can't see.
但人类的活动却是每天、每周、每月都在发生，你看不到，就无法改善。
We wanted to give people the tools to see that change and take action.
我们想要提供一些工具，帮大家看到改变并采取行动。
The beautiful Blue Marble image, taken by the Apollo 17 astronauts in 1972
这张美丽的蓝色弹珠照片是1972年阿波罗17号的航天员所拍摄的，
had helped humanity become aware that we're on a fragile planet.
这张照片让人类明白到我们居住的星球是很脆弱的。
And we wanted to take it to the next level, to give people the tools to take action, to take care of it.
我们还想要更上一层楼，提供工具，让大家能采取行动，关心地球。
Well, after many Apollo projects of our own, launching the largest fleet of satellites in human history,
在我们执行了许多阿波罗计划、发射了人类史上最大的一支卫星舰队后，
we have reached our target. Today, Planet images the entire Earth, every single day. Mission accomplished.
我们终于达成了目标。现在，《星球任务》每天都能为整个地球拍摄影像。任务完成。
Thank you. It's taken 21 rocket launches -- this animation makes it look really simple -- it was not.
谢谢。总共花了21次的火箭发射--这动画使这任务看似简单，但其实不然。
And we now have over 200 satellites in orbit, downlinking their data to 31 ground stations we built around the planet.
现在轨道上的卫星超过200个，向我们建立在地面的31站下传数据。
In total, we get 1.5 million 29-megapixel images of the Earth down each day.
我们每天总共下传150万张29百万画素的地球影像。
And on any one location of the Earth's surface, we now have on average more than 500 images.
在地球表面的任何一个地点，现在平均有超过500张影像。
A deep stack of data, documenting immense change.
非常大量的数据，记录着巨大的改变。
And lots of people are using this imagery. Agricultural companies are using it to improve farmers' crop yields.
而且有许多人正在使用这些影像。农业公司用它们来改善农夫的作物产量。
Consumer-mapping companies are using it to improve the maps you find online.
消费者制图公司用它们来改善你在在线看到的地图。
Governments are using it for border security or helping with disaster response after floods and fires and earthquakes.
政府把它们用在边境安全上，或对洪水、火灾、地震提供灾难应变的帮助。
And lots of NGOs are using it. So, for tracking and stopping deforestation.
还有很多非政府组织使用它们。比如，可以追踪和阻止森林砍伐。
Or helping to find the refugees fleeing Myanmar.
或是协助找到逃离缅甸的难民。
Or to track all the activities in the ongoing crisis in Syria, holding all sides accountable.
或是追踪所有叙利亚正在发生的危机行动，记录各方的责任。
And today, I'm pleased to announce Planet stories.
今天，我很高兴能来发布星球实验室的故事。
Anyone can go online to planet.com open an account and see all of our imagery online.
大家都可以上网造访planet.com，开一个账户，在线看我们所有的影像。
It's a bit like Google Earth, except it's up-to-date imagery, and you can see back through time.
它有点像是Google Earth，只差在它是最新的影像，并且你还能回头看过去的影像。
You can compare any two days and see the dramatic changes that happen around our planet.
你可以比较任何两个日期，看看我们的星球上发生的巨大改变。
Or you can create a time lapse through the 500 images that we have and see that change dramatically over time.
或是你也可以用我们的500张影像来做缩时影片，看看随着时间发生了什么巨大的改变。
And you can share these over social media. It's pretty cool. Thank you.
你还可以把它们分享到社交媒体上。它相当酷。谢谢。
We initially created this tool for news journalists, who wanted to get unbiased information about world events.
我们最初创造这项工具是为了新闻记者，对于全球的事件，他们想取得无偏见的信息。

But now we've opened it up for anyone to use, for nonprofit or personal uses.
但现在我们已经开放给大家使用，可做非营利使用或个人使用。
And we hope it will give people the tools to find and see the changes on the planet and take action.
我们希望这项工具能协助大家找到并发现地球上的改变，进而采取行动。
OK, so humanity now has this database of information about the planet, changing over time.
好，所以人类现在有了关于地球的数据库，有随时间改变的信息。
What's our next mission, what's Mission 2? In short, it's space plus AI.
我们的下一个任务，任务二号是什么？简言之，是太空加上人工智能。
What we're doing with artificial intelligence is finding the objects in all the satellite images.
我们使用人工智能，是要在所有这些卫星影像中找到物体。
The same AI tools that are used to find cats in videos online can also be used to find information on our pictures.
只要使用在在线影片中找出猫咪的人工智能一样的工具，也能被用来寻找我们照片中的信息。
So, imagine if you can say, this is a ship, this is a tree, this is a car, this is a road, this is a building, this is a truck.
想象一下，如果你能分辨，这是一艘船，这是一棵树，这是一台汽车，这是一条道路，这是一栋大楼，这是一台卡车。
And if you could do that for all of the millions of images coming down per day,
若对每天传下来的数百万张影像都能够这么做，
then you basically create a database of all the sizable objects on the planet, every day. And that database is searchable.
基本上你就能建立起一个数据库，内有地球上每天所有物体的信息，相当可观。并且那个数据库能用来搜寻。
So that's exactly what we're doing. Here's a prototype, working on our API. This is Beijing.
那就是我们正在做的事。这是用我们的API（应用程序界面）做的原型。这是北京。
So, imagine if we wanted to count the planes in the airport.
想象一下，如果我们想要知道机场有几架飞机。
We select the airport, and it finds the planes in today's image,
我们选择机场，它就会在今天的影像中找到飞机，
and finds the planes in the whole stack of images before it,
并找到在这之前一大堆影像中的飞机，
and then outputs this graph of all the planes in Beijing airport over time.
接着输出这张图，上面是各时间点北京机场中的飞机。
Of course, you could do this for all the airports around the world.
当然，你可以对地球上所有的机场做这件事。
And let's look here in the port of Vancouver.
咱们来看看这里，温哥华的港口。
So, we would do the same, but this time we would look for vessels.
我们可以做同样的事，但这次是要找船只。
So, we zoom in on Vancouver, we select the area, and we search for ships. And it outputs where all the ships are.
我们将温哥华放大，选择这个区域，搜寻船只。它会输出所有船只的位置。
Now, imagine how useful this would be to people in coast guards who are trying to track and stop illegal fishing.
想象一下，对于海上防卫队，这是多么有用的信息，他们的工作是要追踪和阻止非法捕渔。
See, legal fishing vessels transmit their locations using AIS beacons.
看，合法的捕渔船只，会用AIS信标来传送它们的位置。
But we frequently find ships that are not doing that. The pictures don't lie.
但我们常常会发现没有传送信目标船只。照片不会说谎。
And so, coast guards could use that and go and find those illegal fishing vessels.
所以，海上防卫队能用那信息，去找出非法的捕渔船只。
And soon we'll add not just ships and planes but all the other objects,
不只是船和飞机，之后我们很快就会把其它物体加上去，
and we can output data feeds of those locations of all these objects over time that can be integrated digitally from people's work flows.
我们可以把数据输出来，你可以针对地点、时间区段做输出，这些资料就可以在大家的工作流程中做数字整合。
In time, we could get more sophisticated browsers that people pull in from different sources.
到时候，我们会有更精密的浏览器，让大家可以纳入多重来源的数据。
But ultimately, I can imagine us abstracting out the imagery entirely and just having a queryable interface to the Earth.
但最终，我可以想象我们完全把影像抽取出来，并且有一个能够查询地球的接口。
Imagine if we could just ask, "Hey, how many houses are there in Pakistan? Give me a plot of that versus time."
想象一下，如果我们能问：“嘿，在巴基斯坦有多少间房子？把那数据搭配时间，画张图给我。”
"How many trees are there in the Amazon
“亚马逊有多少棵树？
and can you tell me the locations of the trees that have been felled between this week and last week?"
你能不能告诉我，从上周到这周之间哪些位置的树木被砍伐了？”
Wouldn't that be great? Well, that's what we're trying to go towards, and we call it "Queryable Earth."
那样不是很棒吗？那是我们在试图达成的目标，我们称它为“可查询的地球”。
So, Planet's Mission 1 was to image the whole planet every day and make it accessible.
所以，《星球任务一号》是要做到每天拍摄地球影像，并让这信息能被大家取得。
Planet's Mission 2 is to index all the objects on the planet over time and make it queryable.
《星球任务二号》是要将地球上各时间点的所有物体做索引，并供大家查询。
Let me leave you with an analogy. Google indexed what's on the internet and made it searchable.
最后，我跟大家做个比喻。Google将网络上的东西做了索引，并供大家搜寻。
Well, we're indexing what's on the Earth and making it searchable. Thank you very much.
我们则是将地球上的东西做索引并供大家搜寻。非常谢谢各位。