SlideShare a Scribd company logo
2018 International Conference on Geospatial Information Science
Cloud-powered Machine Learnings
on Geospactial Services
from Your Home to the Earth
Channy Yun
Amazon Web Services Korea
2
C O N T E N T S
1. Deep Learning and Cloud Computing
2. Amazon SageMaker - Fully Managed DL Service
3. Case Study - ML on Geospacial Services
• Digital Globe
• Development Seed
• SpaceNet
4. Geospatial AI nearby You - Amazon Cases
• Amazon Fullfillment, PrimeAir, Go and Alexa
5. Earth on AWS and Research Credits Program
3
이미지 패턴 분석 음성 인식 및
자연어 처리
자율 주행 자동차
4
딥러닝은 컴퓨터들이 인간의 두뇌와
비슷한 모양의 대형 인공 신경망을 형
성하는 일종의 기계 학습 방법
고도화된 학습 알고리즘과 대용량 데이
터를 공급함으로써, "사고"하는 능력과
처리하는 데이터를 "학습"하는 능력을
지속적으로 개선한다. “Deep”이란 시간
이 지나면서 축적되는 신경망의 여러 층
을 의미하며, 신경망의 깊이가 깊어질수
록 성능이 향상된다.
5
컴퓨팅
용량정확도
데이터크기 및 규모
신경망 접근법
다른 기계 학습 방법
© Jeff Dean, Trends and Developments in Deep Learning Research
http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research
6
3% errors
2011
5% errors
humans
26% errors
2016
© Jeff Dean, Trends and Developments in Deep Learning Research
http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research
7
!
MXNetJS in Web Browser W
eb Applications
BlindTool by Joseph Paul Cohen
on Nexus 4 Mobile Application
Deep Drone: Object Detection and
Tracking for Smart Drones on Em
bedded System
https://web.stanford.edu/class/cs231a/
prev_projects_2016/deep-drone-object
__2_.pdf
https://github.com/dmlc/mxnet.js/ http://josephpcohen.com/w/blindto
ol-helping-the-blind-see/
8
-
Fully-managed Deep Learning
Service
Deep Learning
Framework
Nvidia/CUDA, TensorFlow,
PyTourch, MXNet, Keras
Amazon
SageMaker
High-performance GPU (G3/P3),
CPU (C5) Instances
Amazon EC2
Instances
9
0
p3.2xlarge
= $5 per hour
(서울 리전 기준)
p3.2xlarge x 20
= $100 per hour
Spot Instances (75% ↓)
= $30 per hour
11
$aws ec2-run-instances ami-b232d0db
--instance-count 20
--instance-type p3.2xlarge
--region us-east-1
$aws ec2-stop-instances
i-10a64379 i-10a64280 ...
12
https://nucleusresearch.com/research/single/guidebook-tensorflow-aws/
In analyzing the experiences of researchers supporti
ng more than 388unique projects, Nucleus found th
at 88 percent of cloud-based TensorFlow projects
are running on Amazon Web Services.
“
13
1
4.75
8.5
12.25
16
1 4.75 8.5 12.25 16
Speedup(x)
# GPUs
Resnet 152
Inceptin V3
Alexnet
Ideal
-
• P2.16xlarge (8 Nvidia Tesla K80 - 16 GPUs)
• Synchronous SGD (Stochastic Gradient Descent)
91% Efficiency 88% Efficiency
• 16x P2.16xlarge by AWS CloudFormation
• Mounted on Amazon EFS
# GPUs
15
(
)
-
16
, - N , - ,
-
-
N
H J
https://aws.amazon.com/ko/sageamker
18
-
Cache hit rate dropped by nearly 2x
70 % ▶ 40%
19
Direct Connect
80TB / day
Internet
Gateway
Build Model
Feature Extraction
100 PB Archive
User
Application
Cache Hit Rate
Feedback
Optimized
S3 Cache
SM Decision: Cache Image or Not
Cleaned
Feature
Vectors
AWS
Amazon
SageMaker
Jupyter/Pandas
Order
History
Data Ware
house
Imagery
Metadata
-
20
-
“We plan to use Amazon SageMaker to train models
against petabytes of Earth observation imagery
datasets using hosted Jupyter notebooks, so
DigitalGlobe's Geospatial Big Data Platform (GBDX)
users can just push a button, create a model, and
deploy it all within one scalable distributed
environment at scale.”
- Dr. Walter Scott, CTO of Maxar Technologies and founder of DigitalGlobe
21
-
100 PB
Archive
DigitalGlobe
Image Cache
SM Image
Predict
Raster
Data
Access
Jupyter
Notebook
SageMaker Train
SageMaker Host
GBDX Tasks
Vector
Services
User
Application
Explore Orchestrate Consume
Jupyter
Notebook
Real-time random access to all the pixels in DigitalGlobe’s Archive
22
) (() ) ()
Data at rest,
Available in S3
Landsat8
Sentinel2
DigitalGlobe
Archive
Rest API
CallReal-time processing chainOrtho Rectify
Ortho Rectify
Pan Sharpen DRA & Tweak
Endpoint (T
MS/WMS)
23
Rest API
CallReal-time processing chainOrtho Rectify
Ortho Rectify
Pan Sharpen DRA & Tweak
SageMaker
Operator!
Endpoint (T
MS/WMS)
24
) (() ) ()
• Any pixels, any way
you want them
• REST API
• User defined Graphs
• 100s of operators
• Python API
• Gdal Driver
25
-
26
-
27
ICGIS 2018 - Cloud-powered Machine Learnings on Geospactial Services (Channy Yun, AWS)
ICGIS 2018 - Cloud-powered Machine Learnings on Geospactial Services (Channy Yun, AWS)
30
31
-
Training Data
Repository
Synthetic Training Data
via Notebooks
Train via SageMakerRDA
Deploy
Curate
32
https://github.com/developmentseed/skynet-train
Skynet quickly analyze massive amounts of satellite imagery using machine learning
and open data based on AWS EC2 g2 instance and set it up with nvidia-docker.
33
Creating a building classifier in Vietnam using MXNet and SageMaker
Label Maker is to help in extracting insight from satellite imagery that creates training
data for most popular ML frameworks, including Keras, Tensor Flow, and MXNet.
https://github.com/developmentseed/label-maker/blob/master/examples/walkthrough-classification-mxnet-sagemaker.md
34
The SpaceNet Dataset is an open repository of over 5,700+ km2 of satellite imagery
across 5 cities, 520,000+ vectors, and a series of challenges to accelerate geospatial
machine learning.
Automated Mapping
Challenge: Building Extraction
Rounds 1 & 2
Nov. 2016 – Jun. 2017
High Revisit Challenge:
Off-Nadir Object Detection
Launching Spring 2018
Automated Mapping
Challenge:
Road Network Extraction
Nov 2017 – Feb 2018
35
AOI 2 Vegas: Image 1014 AOI 3 Paris: Image 1729 AOI 5 Khartoum: Image 991
https://spacenetchallenge.github.io/
36
No checkout
Store Expriences
Fulfillment
automation and
inventory mana
gement
Automobile
Delivery Drones
Voice driven in
teractions
37
• 0 1
– A I
V K
–
%2, 5
– : 6 % 3
– % 4
• (7 V
%%%7 % )
38
-
• G S
–
1 7 P2
– G ()
0
• 7 6
40
-
• 12 -.0 , 2
– a
J
–
7
• O ( )
W 8 (
htt1s://www.a.a50/.c0./b?/0de=16008589011
41
• 음성 인식을 기반한 가정용 비서 기기, Amazon Echo 최초 출시
• 장난감, 가전, 모바일 기기 등 수 천만대의 Alexa 탑재 기기 출시
• 다양한 음성 비서 서비스 산업 생태계 확대
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Elevation
Models
Aerial
Imagery
Climate
Models
Satellite
Imagery
High-resolution
Radar
aws.amazon.com/earth
aws.amazon.com/earth/research-credits
44
!
AWS
Only
h..p://bi..ly/a/-kr-ml-credi.-
-
e - o n S Ug m M
m R . 21, 31 : r
L A : k a) .( m :
45
1. https://www.slideshare.net/AmazonWebServices/machine-learning-with-earth-
observation-imagery
2. https://www.slideshare.net/AmazonWebServices/altime-machine-learning-on-
satellite-imagery-how-digitalglobe-uses-amazon-sagemaker-to-massively-
scaleup-information-extraction-from-satellite-imagery
3. https://www.slideshare.net/AmazonWebServices/data-boulders-from-space-
how-digitalglobe-uses-aws-to-manage-data
4. http://geospatial.blogs.com/geospatial/2018/04/deep-learning-enables-
automated-extraction-of-building-footprints-and-road-networks-from-
satellite-imagery.html
5. https://aws.amazon.com/blogs/publicsector/how-digitalglobe-uses-amazon-
sagemaker-to-manage-machine-learning-at-scale/
6. http://blog.digitalglobe.com/developers/gbdx-notebooks-and-amazon-
sagemaker-for-systematic-mining-of-geospatial-data/
THANK YOU
2018 International Conference on Geospatial Information Science
윤석찬
아마존웹서비스코리아, 테크에반젤리스트
channyun@amazon.com
http://bit.ly/awskr-feedback
@channyun

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ICGIS 2018 - Cloud-powered Machine Learnings on Geospactial Services (Channy Yun, AWS)

  • 1. 2018 International Conference on Geospatial Information Science Cloud-powered Machine Learnings on Geospactial Services from Your Home to the Earth Channy Yun Amazon Web Services Korea
  • 2. 2 C O N T E N T S 1. Deep Learning and Cloud Computing 2. Amazon SageMaker - Fully Managed DL Service 3. Case Study - ML on Geospacial Services • Digital Globe • Development Seed • SpaceNet 4. Geospatial AI nearby You - Amazon Cases • Amazon Fullfillment, PrimeAir, Go and Alexa 5. Earth on AWS and Research Credits Program
  • 3. 3 이미지 패턴 분석 음성 인식 및 자연어 처리 자율 주행 자동차
  • 4. 4 딥러닝은 컴퓨터들이 인간의 두뇌와 비슷한 모양의 대형 인공 신경망을 형 성하는 일종의 기계 학습 방법 고도화된 학습 알고리즘과 대용량 데이 터를 공급함으로써, "사고"하는 능력과 처리하는 데이터를 "학습"하는 능력을 지속적으로 개선한다. “Deep”이란 시간 이 지나면서 축적되는 신경망의 여러 층 을 의미하며, 신경망의 깊이가 깊어질수 록 성능이 향상된다.
  • 5. 5 컴퓨팅 용량정확도 데이터크기 및 규모 신경망 접근법 다른 기계 학습 방법 © Jeff Dean, Trends and Developments in Deep Learning Research http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research
  • 6. 6 3% errors 2011 5% errors humans 26% errors 2016 © Jeff Dean, Trends and Developments in Deep Learning Research http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research
  • 7. 7 ! MXNetJS in Web Browser W eb Applications BlindTool by Joseph Paul Cohen on Nexus 4 Mobile Application Deep Drone: Object Detection and Tracking for Smart Drones on Em bedded System https://web.stanford.edu/class/cs231a/ prev_projects_2016/deep-drone-object __2_.pdf https://github.com/dmlc/mxnet.js/ http://josephpcohen.com/w/blindto ol-helping-the-blind-see/
  • 8. 8 - Fully-managed Deep Learning Service Deep Learning Framework Nvidia/CUDA, TensorFlow, PyTourch, MXNet, Keras Amazon SageMaker High-performance GPU (G3/P3), CPU (C5) Instances Amazon EC2 Instances
  • 9. 9 0 p3.2xlarge = $5 per hour (서울 리전 기준) p3.2xlarge x 20 = $100 per hour
  • 10. Spot Instances (75% ↓) = $30 per hour
  • 11. 11 $aws ec2-run-instances ami-b232d0db --instance-count 20 --instance-type p3.2xlarge --region us-east-1 $aws ec2-stop-instances i-10a64379 i-10a64280 ...
  • 12. 12 https://nucleusresearch.com/research/single/guidebook-tensorflow-aws/ In analyzing the experiences of researchers supporti ng more than 388unique projects, Nucleus found th at 88 percent of cloud-based TensorFlow projects are running on Amazon Web Services. “
  • 13. 13 1 4.75 8.5 12.25 16 1 4.75 8.5 12.25 16 Speedup(x) # GPUs Resnet 152 Inceptin V3 Alexnet Ideal - • P2.16xlarge (8 Nvidia Tesla K80 - 16 GPUs) • Synchronous SGD (Stochastic Gradient Descent) 91% Efficiency 88% Efficiency • 16x P2.16xlarge by AWS CloudFormation • Mounted on Amazon EFS # GPUs
  • 15. 16 , - N , - , - - N H J https://aws.amazon.com/ko/sageamker
  • 16. 18 - Cache hit rate dropped by nearly 2x 70 % ▶ 40%
  • 17. 19 Direct Connect 80TB / day Internet Gateway Build Model Feature Extraction 100 PB Archive User Application Cache Hit Rate Feedback Optimized S3 Cache SM Decision: Cache Image or Not Cleaned Feature Vectors AWS Amazon SageMaker Jupyter/Pandas Order History Data Ware house Imagery Metadata -
  • 18. 20 - “We plan to use Amazon SageMaker to train models against petabytes of Earth observation imagery datasets using hosted Jupyter notebooks, so DigitalGlobe's Geospatial Big Data Platform (GBDX) users can just push a button, create a model, and deploy it all within one scalable distributed environment at scale.” - Dr. Walter Scott, CTO of Maxar Technologies and founder of DigitalGlobe
  • 19. 21 - 100 PB Archive DigitalGlobe Image Cache SM Image Predict Raster Data Access Jupyter Notebook SageMaker Train SageMaker Host GBDX Tasks Vector Services User Application Explore Orchestrate Consume Jupyter Notebook Real-time random access to all the pixels in DigitalGlobe’s Archive
  • 20. 22 ) (() ) () Data at rest, Available in S3 Landsat8 Sentinel2 DigitalGlobe Archive Rest API CallReal-time processing chainOrtho Rectify Ortho Rectify Pan Sharpen DRA & Tweak Endpoint (T MS/WMS)
  • 21. 23 Rest API CallReal-time processing chainOrtho Rectify Ortho Rectify Pan Sharpen DRA & Tweak SageMaker Operator! Endpoint (T MS/WMS)
  • 22. 24 ) (() ) () • Any pixels, any way you want them • REST API • User defined Graphs • 100s of operators • Python API • Gdal Driver
  • 23. 25 -
  • 24. 26 -
  • 25. 27
  • 28. 30
  • 29. 31 - Training Data Repository Synthetic Training Data via Notebooks Train via SageMakerRDA Deploy Curate
  • 30. 32 https://github.com/developmentseed/skynet-train Skynet quickly analyze massive amounts of satellite imagery using machine learning and open data based on AWS EC2 g2 instance and set it up with nvidia-docker.
  • 31. 33 Creating a building classifier in Vietnam using MXNet and SageMaker Label Maker is to help in extracting insight from satellite imagery that creates training data for most popular ML frameworks, including Keras, Tensor Flow, and MXNet. https://github.com/developmentseed/label-maker/blob/master/examples/walkthrough-classification-mxnet-sagemaker.md
  • 32. 34 The SpaceNet Dataset is an open repository of over 5,700+ km2 of satellite imagery across 5 cities, 520,000+ vectors, and a series of challenges to accelerate geospatial machine learning. Automated Mapping Challenge: Building Extraction Rounds 1 & 2 Nov. 2016 – Jun. 2017 High Revisit Challenge: Off-Nadir Object Detection Launching Spring 2018 Automated Mapping Challenge: Road Network Extraction Nov 2017 – Feb 2018
  • 33. 35 AOI 2 Vegas: Image 1014 AOI 3 Paris: Image 1729 AOI 5 Khartoum: Image 991 https://spacenetchallenge.github.io/
  • 34. 36 No checkout Store Expriences Fulfillment automation and inventory mana gement Automobile Delivery Drones Voice driven in teractions
  • 35. 37 • 0 1 – A I V K – %2, 5 – : 6 % 3 – % 4 • (7 V %%%7 % )
  • 36. 38 - • G S – 1 7 P2 – G () 0 • 7 6
  • 37. 40 - • 12 -.0 , 2 – a J – 7 • O ( ) W 8 ( htt1s://www.a.a50/.c0./b?/0de=16008589011
  • 38. 41 • 음성 인식을 기반한 가정용 비서 기기, Amazon Echo 최초 출시 • 장난감, 가전, 모바일 기기 등 수 천만대의 Alexa 탑재 기기 출시 • 다양한 음성 비서 서비스 산업 생태계 확대
  • 39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Elevation Models Aerial Imagery Climate Models Satellite Imagery High-resolution Radar aws.amazon.com/earth aws.amazon.com/earth/research-credits
  • 40. 44 ! AWS Only h..p://bi..ly/a/-kr-ml-credi.- - e - o n S Ug m M m R . 21, 31 : r L A : k a) .( m :
  • 41. 45 1. https://www.slideshare.net/AmazonWebServices/machine-learning-with-earth- observation-imagery 2. https://www.slideshare.net/AmazonWebServices/altime-machine-learning-on- satellite-imagery-how-digitalglobe-uses-amazon-sagemaker-to-massively- scaleup-information-extraction-from-satellite-imagery 3. https://www.slideshare.net/AmazonWebServices/data-boulders-from-space- how-digitalglobe-uses-aws-to-manage-data 4. http://geospatial.blogs.com/geospatial/2018/04/deep-learning-enables- automated-extraction-of-building-footprints-and-road-networks-from- satellite-imagery.html 5. https://aws.amazon.com/blogs/publicsector/how-digitalglobe-uses-amazon- sagemaker-to-manage-machine-learning-at-scale/ 6. http://blog.digitalglobe.com/developers/gbdx-notebooks-and-amazon- sagemaker-for-systematic-mining-of-geospatial-data/
  • 42. THANK YOU 2018 International Conference on Geospatial Information Science 윤석찬 아마존웹서비스코리아, 테크에반젤리스트 channyun@amazon.com http://bit.ly/awskr-feedback @channyun