The document discusses efficient exploitation of remote sensing data. It summarizes Grega Milcinksi's presentation on Sentinel Hub, a platform for accessing and processing satellite data. It notes that large volumes of remote sensing data are created daily but pre-processing into "data cubes" limits flexibility. The document recommends processing data on-demand using cloud computing. Sentinel Hub is highlighted as an example that provides open data access through APIs and applications using AWS services. It processes over 50 million requests per month from various data sources. The document concludes that public data should be openly available in the cloud and reasonable business models are needed for commercial data.

1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Grega Milcinksi
General Manager, Sinergise
Joel Cumming
CTO, SkyWatch
Making Sense of Remote Sensing

2. Grega Milcinski, Sinergise
Sentinel Hub – efficient
exploitation of remote
sensing data

3. About Sinergise
• 50 person small business
• Large geospatial applications
• Depends on own cash flow
• Sentinel Hub, Sentinel Playground, EO Browser

4. Large volumes of data are created every day

5. Modis, 2018
Powered by

6. “Data cubes” options
• Pre-processed images
•“Just images”
•Fixed composite settings (true color, bands 721, ...)
•Fixed projections (Geographic, Web Mercator, Polar)
•Fixed processing steps (lack of flexibility)
•Storage and compute (regardless of the use)
• Pre-processed tiles
•Storage and compute
•Some limitations remain (lack of flexibility)

7. Recommendations
• Only process to the level widely supported by the community
• COG is a data cube (of some sort)
•JPEG2000 and HDF as well
• Further steps can be executed on-the-fly
•With the help of fast storage, on-demand compute
•Decompression, reprojection, filtering, mosaicking, compositing, analysis,
output encoding, etc.
•Not good for everything... e.g. mass processing

8. WMS
Commercial EO data
Aerial imagery (drone, plane)
Other raster data
Open EO data - Sentinel-1, Sentinel-2,
Landsat, etc.
WMTS
Machine learning
API
WCS
Scripting
(R, Python, ENVI…)
Web / Mobile apps
Desktop (QGIS, ArcGIS…)
Cloud GIS

9. http://apps.sentinel-hub.com/sentinel-playground/

10. Time-lapse
http://apps.sentinel-hub.com/eo-browser/

11. Statistical API, time-series

12. Multi-temporal analysis

13. Orthorectification

14. Easy to set-up

15. Supported Data sources
• Currently available
Sentinel-1 GRD (global archive since 1/5/17)
Sentinel-2 (full global archive)
Sentinel-3 (full global archive)
Landsat-8 USGS (global archive)
Landsat-5, 7, 8 (ESA Archive)
Envisat MERIS (full global archive)
MODIS Terra and Aqua
DEM – SRTM30
Planet and RapidEye (limited due to business models)
• Up to date!

16. Performance

17. System stats
• 50 Million requests per month (May 2018, growing 10-20% per month)
• 0.53 Bn data access requests (cca 50 PB of data)
• 5.5 TB data transfer out

18. System design
AWS Elastic Load Balancer
Layer7 Load Balancers
User
(2x m3.medium)
Data processor
c5.2xlarge
• 8x on demand
• up to 10 spot
Catalogue
Configuration
2x m5.large
Index
10TB
S3
Data
3PB
OGCLB
DB
1x m4.large
2x i3.xlarge

20. System design
AWS Elastic Load Balancer
Layer7 Load Balancers
User
(2x m3.medium)
c5.2xlarge
• 8x on demand
• up to 10 spot
Catalogue &
Configuration
2x m5.large
Index
10TB
S3
Data
3PB
LB
DB
1x m4.large
2x i3.xlarge
OGC
Stat renderer
AWS lambda
Data processor

21. System stats

22. Lessons learned - infrastructure
• As little storage as needed
• Build system close to the data (performance + costs)
• Requester pays is still an OK compromise
• SPOTs are cheap (3.5x cheaper than on-demand) yet volatile
• S3 is really fast (bucket sharing makes it faster)
• Many workers have better throughput to S3
• Lambda is not good for everything

23. WMS
Commercial EO data – WorldWind, GeoEye,...
Aerial imagery (drone, plane)
Other raster data
Open EO data - Sentinel-1, Sentinel-2,
Landsat, etc.
WMTS
Machine learning
API
WCS
Scripting
(R, Python, ENVI…)
Web / Mobile apps
Desktop (QGIS,, ArcGIS…)
Cloud GIS

24. AeroView, aerobotics.co.zacropsat.dkCLAAS CropViewsatamap.com.au

25. SecureWatch

26. Landslides

27. Floods

28. Drought

29. Sea-ice monitoring

30. Wildfires

31. Volcano eruptions

32. Florida Keys after Irma, 13/9/17,
Zachary M. Labe, @ZLabe, Copernicus

34. TBC – exploiting the power of ML
https://github.com/sentinel-hub/sentinelhub-py

35. TBC – exploiting the power of ML
https://github.com/sentinel-hub/eo-learn

36. TBC – exploiting the power of ML

37. Lessons learned and remaining challenges
• Public datasets are really cool
• With commercial providers minimum order is often the issue, not price per
sq.km (pay per use, revenue sharing)
• Data providers are not as consistent as one would like
• Redesign of existing algorithms made for whole tiles
• Setting up the system is just the beginning of the work

38. Summary
• For public institutions creating remote sensing data
•Put them in the cloud
•Make them openly and directly available
•Use COG or similar
•Data portals vs. Open dataset
• For commercial entities creating remote sensing data:
•Put them in the cloud
•Make them directly available under reasonable business model
•Use COG or similar

39. More info
• http://sentinel-hub.com/
• http://apps.sentinel-hub.com/eo-browser/
• http://apps.sentinel-hub.com/sentinel-playground/
• https://sentinel-hub.github.io/custom-scripts/
• https://github.com/sentinel-hub
• https://github.com/sentinel-hub/eo-learn/
Thanks

41. Joel Cumming
CTO,

42. Source: NASA VIIRS, Creative Commons
1958: Sputnik
(USSR)
1959: Explorer-1
(USA)

43. Source: ESA Sentinel-2

44. The Past
The Future

45. 1000x lower launch costs
100x smaller satellites
100x lower sensor costs
500,000x lower data costs

46. Historically, data acquisition and
preparation has been very
painful

47. We believe that everyone on the
planet should benefit from
satellite data

48. #SXSW2018
Old:
New:

49. The SkyWatch EarthCache
Platform

51. If you had no legacy, how would
you build?

52. Our Engineering Principles

53. Microservices Architecture

54. 100% Serverless

55. Freedom and Autonomy
(with a paved road)

56. Our Serverless Microservice
Blueprint

57. Simple Serverless Microservice
API Gateway
Lambda
DynamoDB

58. SkyWatch Serverless Template
Authorization
Autoscaling limits
Ops Logging
Analytics Logging Retry Logic
Security
REST/Lambda Invoke Abstraction
Security
Warming Plugin
Discovery
Service
Lambda
DynamoDB
X-Ray

59. Other AWS Serverless Tools

60. Bottom Line:
Our infrastructure is our code

61. Microservices Data Flow

62. Benefits

63. Zero Wasted Provisioning
(absolute, repeatable cost per result)

64. No Ops Team
(you build it, you own it)

65. Customer Focused Development
(not system focused)

66. Our Road Ahead

67. Serverless RDBMS
(Aurora)

68. Extending GraphQL
(AppSync)

69. State Machines
(Step Functions)

70. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Thank you!