Lalitesh Katragadda: Making maps to fight disaster, build economies

36,773 views ・ 2010-01-13

TED


μ•„λž˜ μ˜λ¬Έμžλ§‰μ„ λ”λΈ”ν΄λ¦­ν•˜μ‹œλ©΄ μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€.

λ²ˆμ—­: Chanmin Park κ²€ν† : Jonghoon JANG
00:16
In 2008, Cyclone Nargis devastated Myanmar.
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2008년에 사이클둠 λ‚˜λ₯΄κΈ°μŠ€κ°€ λ―Έμ–€λ§ˆλ₯Ό μ²˜μ°Έν•˜κ²Œ νŒŒκ΄΄ν–ˆμŠ΅λ‹ˆλ‹€.
00:21
Millions of people were in severe need of help.
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수백만의 μ‚¬λžŒλ“€μ΄ κ°„μ ˆνžˆ 도움을 μ›ν–ˆμ£ .
00:25
The U.N. wanted to rush people and supplies to the area.
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UN은 ν•΄λ‹Ή 지역에 인λ ₯κ³Ό 물자λ₯Ό μ„œλ‘˜λŸ¬ νˆ¬μž…ν•˜λ €κ³  ν–ˆμŠ΅λ‹ˆλ‹€.
00:29
But there were no maps, no maps of roads,
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ν•˜μ§€λ§Œ 지도가 μ—†μ—ˆμŠ΅λ‹ˆλ‹€.
00:32
no maps showing hospitals, no way for help to reach the cyclone victims.
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λ„λ‘œκ°€ λ‚˜μ˜¨, λ³‘μ›μ˜ μœ„μΉ˜κ°€ λ‚˜μ˜¨, 사이클둠 ν”Όν•΄μžλ“€μ—κ²Œ μ ‘κ·Όν•  길이 λ‚˜μ˜¨ 지도가 μ—†μ—ˆμ–΄μš”.
00:37
When we look at a map of Los Angeles or London,
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LAλ‚˜ 런던의 지도λ₯Ό 보면
00:40
it is hard to believe
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κ·Έλ ‡κ²Œ ꡬ체적인 지리정보가 λ‹΄κΈ΄ 지도가 μžˆλŠ” 뢀뢄이
00:43
that as of 2005, only 15 percent of the world
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2005λ…„ κΈ°μ€€ μ „μ„Έκ³„μ˜ 15νΌμ„ΌνŠΈμ— λΆˆκ³Όν•˜λ‹€λŠ” 사싀을
00:46
was mapped to a geo-codable level of detail.
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λ―ΏκΈ° μ–΄λ €μš°μ‹€ κ²λ‹ˆλ‹€.
00:49
The U.N. ran headfirst into a problem
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UN이 세계 λŒ€λ‹€μˆ˜μ˜ μ‚¬λžŒλ“€μ΄ κ²ͺκ³  μžˆλŠ”
00:52
that the majority of the world's populous faces:
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이 λ¬Έμ œμ— κ°‘μžκΈ° μ§λ©΄ν–ˆμŠ΅λ‹ˆλ‹€.
00:54
not having detailed maps.
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ꡬ체적인 지도가 μ—†λ‹€λŠ” λ¬Έμ œμš”.
00:56
But help was coming.
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ν•˜μ§€λ§Œ λ„μ›€μ˜ 손길이 λ‚˜νƒ€λ‚¬μŠ΅λ‹ˆλ‹€.
00:58
At Google, 40 volunteers
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κ΅¬κΈ€μ—μ„œ λ§ˆν” λͺ…μ˜ μžμ›λ΄‰μ‚¬μžλ“€μ΄
01:00
used a new software
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μƒˆλ‘œμš΄ μ†Œν”„νŠΈμ›¨μ–΄λ₯Ό μ‚¬μš©ν•΄μ„œ
01:03
to map 120,000 kilometers of roads,
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12만 ν‚¬λ‘œλ―Έν„°μ— λ‹¬ν•˜λŠ” λ„λ‘œμ™€
01:06
3,000 hospitals, logistics and relief points.
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3천개의 병원, λ¬Όλ₯˜ 및 ꡬ호 μ§€μ μ˜ 지도λ₯Ό λ§Œλ“€μ—ˆμŠ΅λ‹ˆλ‹€.
01:09
And it took them four days.
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여기에 λ‚˜ν˜μ΄ κ±Έλ Έμ£ .
01:11
The new software they used? Google Mapmaker.
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μ–΄λ–€ μ†Œν”„νŠΈμ›¨μ–΄μ˜€μ„κΉŒμš”? ꡬ글 λ§΅λ©”μ΄μ»€μž…λ‹ˆλ‹€.
01:14
Google Mapmaker is a technology that empowers each of us
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ꡬ글 λ§΅λ©”μ΄μ»€λŠ” μš°λ¦¬κ°€ μ§€μ—­μ μœΌλ‘œ μ•Œκ³  μžˆλŠ” 정보듀을
01:17
to map what we know locally.
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각자 μ§€λ„λ‘œ 그릴 수 μžˆλ„λ‘ μ§€μ›ν•˜λŠ” κΈ°μˆ μž…λ‹ˆλ‹€.
01:20
People have used this software
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μ‚¬λžŒλ“€μ€ 이 μ†Œν”„νŠΈμ›¨μ–΄λ₯Ό
01:22
to map everything from roads to rivers,
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λ„λ‘œμ—μ„œ κ°•κΉŒμ§€, ν•™κ΅μ—μ„œ 지역 μƒκΆŒκΉŒμ§€,
01:24
from schools to local businesses,
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그리고 λΉ„λ””μ˜€ λŒ€μ—¬μ μ—μ„œ κ΅¬λ©κ°€κ²Œμ— 이λ₯΄λŠ”
01:27
and video stores to the corner store.
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λͺ¨λ“  것을 μ§€λ„λ‘œ κ·Έλ¦¬λŠ”λ° μ‚¬μš©ν•©λ‹ˆλ‹€.
01:30
Maps matter.
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μ§€λ„λŠ” μ€‘μš”ν•©λ‹ˆλ‹€.
01:32
Nobel Prize nominee Hernando De Soto
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노벨상 후보인 에λ₯΄λ‚œλ„ 데 μ†Œν† λŠ”
01:34
recognized that the key to economic liftoff
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아직 μžλ³Έν™”λ˜μ§€ μ•Šμ€ λ§‰λŒ€ν•œ 땅을 κ°œλ°œν•˜λŠ” 것이
01:36
for most developing countries
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λŒ€λΆ€λΆ„μ˜ κ°œλ°œλ„μƒκ΅­μ˜ 경제λ₯Ό
01:38
is to tap the vast amounts of uncapitalized land.
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μΌμœΌν‚€κΈ° μœ„ν•œ μ—΄μ‡ κ°€ 될 것이라고 λ§ν–ˆμŠ΅λ‹ˆλ‹€.
01:41
For example, a trillion dollars
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이λ₯Όν…Œλ©΄ μΈλ„λ§Œν•΄λ„
01:44
of real estate remains uncapitalized in India alone.
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μ—„μ²­λ‚œ 규λͺ¨μ˜ 뢀동산이 아직 μžλ³Έν™”λ˜μ§€ λͺ»ν–ˆμ£ .
01:47
In the last year alone,
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μ§€λ‚œ ν•œ ν•΄ λ™μ•ˆμ—λ§Œ
01:49
thousands of users in 170 countries
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170μ—¬κ°œκ΅­ 수천λͺ…μ˜ μ‚¬μš©μžλ“€μ΄
01:53
have mapped millions of pieces of information,
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수백만개의 정보듀을 지도에 μž…λ ₯ν•΄μ„œ
01:56
and created a map of a level of detail never thought viable.
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λˆ„κ΅¬λ„ μ„±κ³΅ν•˜λ¦¬λΌ μƒκ°ν•˜μ§€ λͺ»ν•œ μˆ˜μ€€μ˜ μƒμ„Έν•œ 지도λ₯Ό λ§Œλ“€μ—ˆμŠ΅λ‹ˆλ‹€.
01:59
And this was made possible by
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그리고 이것은 세계 κ³³κ³³ μ–΄λŠκ³³μ—λ‚˜ μžˆλŠ”
02:01
the power of passionate users everywhere.
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열정적인 μ‚¬μš©μžλ“€μ˜ 힘으둜 μ‹€ν˜„λ˜μ—ˆμ£ .
02:05
Let's look at some of the maps
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μ‚¬μš©μžλ“€μ— μ˜ν•΄ μ œμž‘λœ
02:08
being created by users right now.
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지도듀을 μ’€ λ³΄μ—¬λ“œλ¦¬κ² μŠ΅λ‹ˆλ‹€.
02:11
So, as we speak, people are mapping the world
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자, 말씀 λ“œλ¦° κ²ƒμ²˜λŸΌ 이 170μ—¬κ°œκ΅­μ˜
02:13
in these 170 countries.
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μ‚¬μš©μžλ“€μ΄ 세계λ₯Ό μ§€λ„λ‘œ ν‘œν˜„ν•˜κ³  μžˆμŠ΅λ‹ˆλ‹€.
02:15
You can see Bridget in Africa who just mapped a road in Senegal.
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μ•„ν”„λ¦¬μΉ΄μ—μ„œ λΈŒλ¦¬μ§“μ΄ μ„Έλ„€κ°ˆμ— μžˆλŠ” 길을 μ§€λ„λ‘œ ν‘œν˜„ν•œ κ²ƒμž…λ‹ˆλ‹€.
02:21
And, closer to home, Chalua, an N.G. road in Bangalore.
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쑰금 더 이μͺ½μœΌλ‘œ 와보죠. 찰루아가 κ·Έλ¦° 인도 방갈둜λ₯΄μ— μžˆλŠ” 내셔널 μ§€μ˜€κ·Έλž˜ν”½ λ‘œλ“œμž…λ‹ˆλ‹€.
02:26
This is the result of computational geometry,
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μ΄λŠ” μ „μ‚°κΈ°ν•˜ν•™κ³Ό
02:29
gesture recognition, and machine learning.
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λͺΈμ§“ 인식 그리고 κΈ°κ³„ν•™μŠ΅μ˜ κ²°κ³Όλ¬Όμž…λ‹ˆλ‹€.
02:32
This is a victory of thousands of users,
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수백개 λ„μ‹œμ— μžˆλŠ”
02:34
in hundreds of cities,
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수천λͺ…μ˜ μ‚¬μš©μžλ“€μ΄
02:36
one user, one edit at a time.
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ν•œ μ‚¬λžŒμ΄ ν•œ λ²ˆμ— ν•˜λ‚˜μ”© λ§Œλ“€μ–΄ λ‚˜κ°„ μŠΉλ¦¬μž…λ‹ˆλ‹€.
02:38
This is an invitation to the 70 percent
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지ꡬ μƒμ—μ„œ μ§€λ„λ‘œ λ§Œλ“€μ–΄μ§€μ§€ μ•Šμ€
02:42
of our unmapped planet.
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λ‚˜λ¨Έμ§€ 70%의 μ„ΈμƒμœΌλ‘œ μ΄ˆλŒ€ν•©λ‹ˆλ‹€.
02:44
Welcome to the new world.
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μƒˆλ‘œμš΄ 세상에 μ˜€μ‹  것을 ν™˜μ˜ν•©λ‹ˆλ‹€.
02:46
(Applause)
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(λ°•μˆ˜)
이 μ›Ήμ‚¬μ΄νŠΈ 정보

이 μ‚¬μ΄νŠΈλŠ” μ˜μ–΄ ν•™μŠ΅μ— μœ μš©ν•œ YouTube λ™μ˜μƒμ„ μ†Œκ°œν•©λ‹ˆλ‹€. μ „ 세계 졜고의 μ„ μƒλ‹˜λ“€μ΄ κ°€λ₯΄μΉ˜λŠ” μ˜μ–΄ μˆ˜μ—…μ„ 보게 될 κ²ƒμž…λ‹ˆλ‹€. 각 λ™μ˜μƒ νŽ˜μ΄μ§€μ— ν‘œμ‹œλ˜λŠ” μ˜μ–΄ μžλ§‰μ„ 더블 ν΄λ¦­ν•˜λ©΄ κ·Έκ³³μ—μ„œ λ™μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€. λΉ„λ””μ˜€ μž¬μƒμ— 맞좰 μžλ§‰μ΄ μŠ€ν¬λ‘€λ©λ‹ˆλ‹€. μ˜κ²¬μ΄λ‚˜ μš”μ²­μ΄ μžˆλŠ” 경우 이 문의 양식을 μ‚¬μš©ν•˜μ—¬ λ¬Έμ˜ν•˜μ‹­μ‹œμ˜€.

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