Valle de la Luna, Argentina – July 19, 2016

Cloud 2.0 for PlanetScope Imagery

Alan Collison (IZ)

Amit Kapadia (KJ)

Anudeep Kanwar (KJ)

Ian Hansen (JM)

Karim Lenhard (IZ)

Jesus Martinez Manso (GE)

Michael Schwarz (MAV)

Michael Weisman (FW)

Maurice Schoenert (KJ)

Tim Patelscheck (MAV)

Yohan Yi (BB)

Ignacio Zuleta

Kelsey Jordahl

Frank Warmerdam

Massimiliano Vitale

Brian Brown

Gopal Erinjippurath

Jeff Marion

Austin Schwark

Creon Levit

Chris Holmes

Special Recognition to ...

Jeddah, Saudi Arabia

Introduction, Motivations and Objectives

Outline

Take-aways from surveys of ...

• LandSat-8

• Sentinel-2

• ClimateCorp Patent

• DG Patent

And … how good is it?

So … what’s Planet doing?

Preparing an ARD Stack

Chris Holmes "Analysis Ready Data Defined" blog

Survey of LandSat-8

Key Take-Aways

1. Users desire different

classifications so that their

applications are able to determine

which pixels can be used

1. Users desire confidence measures

1. Decision trees yield good

performance

1. Expect better performance with

additional bands

Cloud Mapping in LS-8

Quality Band

Landsat Cloud Cover Assessment Validation Datasets

Survey of Sentinel-2

Example of Sentinel-2 Cloud and

Cirrus Map

Key Take-Aways

1. Users desire more than just opaque

and cirrus clouds

1. Users prefer masks over polygons

1. Expect better performance with

additional bands

Improving Cloud Detection with Machine Learning

Survey of Sentinel-2

Clouds and Cloud shadows in RapidEye and Deimos imagery

5-stage algorithm

1. Identify “cloud seeds” using high-precision low-recall classifier

2. Apply morphological operators to remove false positives

3. Apply clustering to “grow” cloud seeds into full clouds

4. Compute cloud heights by exploiting the parallax between the

bands

5. Compute cloud shadows using sun illumination and sensor viewing

geometry

Climate Corporation PatentCloud detection on remote sensing imagery

Clouds in DigitalGlobe imagery (QB, WV-1/2/3/4, IKONOS, GE-1)

Can operate on PAN or PAN+MS imagery

Relies on “spatial texture” for detection

5-stage algorithm

1. Uses a “cloud dictionary” of pictures of cloud

2. Uses a “ground dictionary” of pictures of ground

3. Apply ML training to identify words in truthed imagery using Sparse

Coding for Dictionary Learning

4. Apply serpentine scan of imagery to obtain cloud/ground scores

5. Apply Max-flow/Min-cut Segmentation and Edge Growing to obtain

final cloud/clear masks

6. Clean-up operations to eliminate unrealistically small regions

DigitalGlobe Patenthttps://patentscope.wipo.int/search/en/detail.jsf?docId=US219388890

Planet Approach

Manifest

Machine Learning TrainingThresholding

Predicted Label

Consistency Matrix

Machine Learning TrainingThresholding

Predicted Label

Combined haze and

cloud correctly

predicted 74% of the

time … for this

subset!

Confusion Matrix

Machine Learning TrainingThresholding

Predicted Label

Haze-Optimized Transform

Assessment of Cloud 2.0 Metadata for Data Discovery

Compare live

Compare live

Assessment of Cloud 2.0 Metadata by Mosaics TeamPrepared by Maurice Schoenert

Mons, Belgium Democratic Republic of the Congo

Time-Series Data DiscoveryImperial, CA

30-50%

Time-Series Data AnalysisImperial, CA

Mis-classified cloud or haze

Mis-classified shadow

Comparison with LandSat-8

Comparison with Sentinel-2

Quote:

Pioneer/Granular thinks our cloud masking is better than theirs and

offers cost savings over their current set up, so they are going to use our

new masking going forward. They had nothing but positive things to

say!

A Customer PerspectivePioneer/Granular

Jeddah, Saudi Arabia

Check your Truth Data for consistency!

Zero is NOT missing data!

Substantial improvement over Cloud 1.0

Working to hook Cloud 2.0 into “last mile” of

delivery to end-users

Starting work on SkySat

Lessons Learned, Conclusions and Next

Steps

keith@planet.com