Four years ago, here at TED, I announced Planet's Mission 1: to launch a fleet of satellites that would image the entire Earth, every day, and to democratize access to it.
﻿4年前，就在TED的讲台上， 我公布了地球任务1号： 一个为了每天拍摄地球全貌 而发射一系列卫星的任务， 并让大众能获得这些信息。
The problem we were trying to solve was simple. Satellite imagery you find online is old, typically years old, yet human activity was happening on days and weeks and months, and you can't fix what you can't see. We wanted to give people the tools to see that change and take action. The beautiful Blue Marble image, taken by the Apollo 17 astronauts in 1972 had helped humanity become aware that we're on a fragile planet. And we wanted to take it to the next level, to give people the tools to take action, to take care of it.
我们要解决的问题很简单， 网上你能找到的卫星 图片很旧，甚至早就过时了， 但是人类活动每天、 每周、每月都在发生， 我们不能解决自己看不见的问题。 我们想要给人们工具去 看见变化，并且采取行动。 阿波罗17号的宇航员在1972年 照下了美丽的蓝色星球图片， 帮助人们了解到，我们生活在 一个脆弱的星球上。 我们想要继续这项事业， 为人们提供保护地球的工具。
Well, after many Apollo projects of our own, launching the largest fleet of satellites in human history, we have reached our target. Today, Planet images the entire Earth, every single day. Mission accomplished.
在完成了多个我们自己的阿波罗任务， 发射了人类历史上数量 最多的一组卫星后， 我们完成了既定目标。 今天，Planet每天都在 记录着地球的全貌。 任务完成。
(Applause)
（掌声）
Thank you. It's taken 21 rocket launches -- this animation makes it look really simple -- it was not. And we now have over 200 satellites in orbit, downlinking their data to 31 ground stations we built around the planet. In total, we get 1.5 million 29-megapixel images of the Earth down each day. And on any one location of the Earth's surface, we now have on average more than 500 images. A deep stack of data, documenting immense change.
谢谢。 这个任务经历了21次火箭发射—— 这个动画让整个过程看起来 很轻松——但事实并非如此。 现在我们有超过200个卫星 在轨道上环绕运行， 将搜集到的数据发送到31个全球站点。 每天，我们总计得到150万张 29兆像素的地球表面图片。 并且，在地球表面任意一点， 我们现在拥有平均超过500张图片。 一组巨大的数据， 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. Consumer-mapping companies are using it to improve the maps you find online. Governments are using it for border security or helping with disaster response after floods and fires and earthquakes. And lots of NGOs are using it. So, for tracking and stopping deforestation. Or helping to find the refugees fleeing Myanmar. Or to track all the activities in the ongoing crisis in Syria, holding all sides accountable.
很多人在使用这些图片。 农业企业用他们来提高农民的产量。 商业地图公司用他们来 提高地图的精度。 政府用他们来监管边疆安全， 或是应对自然灾害， 比如洪水、火灾或地震。 很多非政府组织也在用它们 去追踪并阻止森林砍伐， 帮助找到逃离缅甸的难民， 或追踪叙利亚危机中的活动， 以令各方势力负责。
And today, I'm pleased to announce Planet stories. Anyone can go online to planet.com open an account and see all of our imagery online. It's a bit like Google Earth, except it's up-to-date imagery, and you can see back through time. You can compare any two days and see the dramatic changes that happen around our planet. Or you can create a time lapse through the 500 images that we have and see that change dramatically over time. And you can share these over social media. It's pretty cool.
今天，我有幸能够宣布 Planet stories的上线。 任何人都可以登陆planet.com， 创建一个账户，就能 在网上看到所有的图片。 这是一个实时版的 Google Earth， 你还可以看到历史数据。 你可以比较任何两天， 然后发现我们的星球 发生的巨大改变。 或者你可以用这500张照片 创造一个延时视频， 去显示该地点随着时间 发生的巨大变化， 你还可以把它们 分享到社交网络上。 很强大的工具。
(Applause)
（掌声）
Thank you.
谢谢。
We initially created this tool for news journalists, who wanted to get unbiased information about world events. But now we've opened it up for anyone to use, for nonprofit or personal uses. And we hope it will give people the tools to find and see the changes on the planet and take action. OK, so humanity now has this database of information about the planet, changing over time.
我们最初为新闻工作者 创造了这个工具， 因为他们想得到对于世界 不带偏见的信息。 但现在我们把它向公众开放， 作为非盈利或个人用途。 我们希望这个工具能够让人们 发现地球发生的变化， 并做出改变。 那么人类现在有了这个不断变化的 地球数据库。
What's our next mission, what's Mission 2? In short, it's space plus AI. What we're doing with artificial intelligence is finding the objects in all the satellite images. The same AI tools that are used to find cats in videos online can also be used to find information on our pictures. 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. And if you could do that for all of the millions of images coming down per day, then you basically create a database of all the sizable objects on the planet, every day. And that database is searchable.
我们的下一个任务是什么呢？ 简而言之， 就是空间加上人工智能。 我们利用人工智能 检索卫星图片中的事物。 网上用来在视频中标记猫狗的AI工具， 同样可以用来处理我们的照片。 想象这里有艘船，这里有棵树， 这是辆车，这是条路， 这是个大楼，这是个卡车。 如果你能够对 每天产生的几百万张图片 这样处理， 就基本上创造出了一个数据库， 包含了地球上每天存在的有形事物。 并且这个数据库是可以被搜索的。
So that's exactly what we're doing. Here's a prototype, working on our API. This is Beijing. So, imagine if we wanted to count the planes in the airport. We select the airport, and it finds the planes in today's image, and finds the planes in the whole stack of images before it, and then outputs this graph of all the planes in Beijing airport over time. Of course, you could do this for all the airports around the world. And let's look here in the port of Vancouver. So, we would do the same, but this time we would look for vessels. So, we zoom in on Vancouver, we select the area, and we search for ships. And it outputs where all the ships are.
这就是我们在做的事。 这是一个使用了我们API的原型。 这是北京。 如果我们想要统计机场的飞机数量， 只需要选择机场， 程序就会检索出今天照片中的飞机， 和以前所有照片中的飞机， 然后它生成了这张标记了 北京机场飞机的统计图。 当然，你可以对世界上 任何一个机场这样操作。 让我们来看看温哥华的一个港口。 我们用同样的流程来统计船只。 放大并选中温和华，搜索这片区域， 然后我们搜索船只， 就会得到船只的情况。
Now, imagine how useful this would be to people in coast guards who are trying to track and stop illegal fishing. See, legal fishing vessels transmit their locations using AIS beacons. But we frequently find ships that are not doing that. The pictures don't lie. And so, coast guards could use that and go and find those illegal fishing vessels. And soon we'll add not just ships and planes but all the other objects, 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. In time, we could get more sophisticated browsers that people pull in from different sources.
想象一下这将为负责 追踪并组织非法捕鱼的 海岸保卫人员提供多么大的帮助。 合法的捕鱼船只 用AIS灯塔传达他们的位置， 但我们经常发现违反规则的船只。 图片不会撒谎。 所以，海岸保卫人员可以利用这个信息 来发现非法船只。 我们将会很快加入不局限于 飞机船只的其他对象， 并且我们可以生成这些地点的 对象数据流， 从人们的工作流程中 进行数字化集成。 未来我们还可以建立一个 更复杂的浏览器， 让人们放入不同来源的信息。
But ultimately, I can imagine us abstracting out the imagery entirely and just having a queryable interface to the Earth. Imagine if we could just ask, "Hey, how many houses are there in Pakistan? Give me a plot of that versus time." "How many trees are there in the Amazon and can you tell me the locations of the trees that have been felled between this week and last week?" Wouldn't that be great?
但是最终，我们可以把图像完全抽象化， 产生一个可检索的地球表面界面。 想象一下我们可以这样问， “巴基斯坦有多少栋楼房？ 对时间做个统计图。” “亚马逊有多少树？ 还有从上周到这周 倒下的树的地点？” 这不是很棒吗？
Well, that's what we're trying to go towards, and we call it "Queryable Earth." So, Planet's Mission 1 was to image the whole planet every day and make it accessible. Planet's Mission 2 is to index all the objects on the planet over time and make it queryable.
这就是我们想要达到的目标， 我们叫它“可检索地球”。 地球任务1号负责 每天记录地球表面的图像， 并对大众开放。 地球任务2号负责 对地球上所有事物编码， 生成检索信息。
Let me leave you with an analogy. Google indexed what's on the internet and made it searchable. Well, we're indexing what's on the Earth and making it searchable.
不妨这样类比， Google把互联网上的事物编码， 建立了搜索引擎， 我们要将地球上所有事物编码， 同样方便大家查询。
Thank you very much.
非常感谢！
(Applause)
（掌声）