1. Machine Learning & Earth Observation
Chad Fish, Chief Operating Officer
22 April 2017

2. 2030 Growing Need 30% More Water 40% More Energy 50% More Food
Growing Population Places Increasing Demands on Food- Water-Energy Nexus
30% More
Water
2030
40% More
Energy
50% More
Food
© OmniEarth, Inc. 2017 2

3. ” “ UN Food & Agriculture Organization
The Water-Energy-Food Nexus: A new approach in support of food security and sustainable agriculture ”
Satellite observations … are necessary to begin understanding the complex feed-back processes between the natural environment and human activities. “

4. Many Have Started Down This Path
Numerous Climate-Related Satellites Have Been Launched Over the Past 40 Years
SMAP
SEASTAR
FLEX
LandSat
Sentinel
Global
Precipitation
Measurement
GLORY
CALIPSO

5. The Problem
Processes Are Interrelated But Our Measurement Systems Are Not

6. The Problem
No Measurement System Sees the Entire Earth at One Time

7. Constellations Improve Understanding
Global Synoptic Measurements Enable Understanding of Transport Mechanisms and the Complex Feedback Processes Between the Natural Environment and Human Activities

8. but create
terabytes of
data daily

9. Imagery + Machine Learning
Actionable Intelligence for Customers Who Are Not Data Scientists, Analysts or Engineers
Imagery
Location 1, Time t1
Location 2, Time t2
Training Feature Sets
Houses
Roads
Water
Bridges
Crops
Planes
Trails
Deep Learning
OmniEarth Classifier(s) + Model Zoo (open source)
Output Statistics
Image-based classification
Numbers of things (e.g. numbers of cars)
Lookup of things (e.g. locations of all bridges in the US of certain type)
Numbers of new things (development, infrastructure)
Event recognition for national security
Open Source Community
Leveraged Assets
Imagery
Location 1, Time t1 + 1
Location 2, Time t2 + 1
Use OmniEarth Classifiers

10. Application of Artificial Intelligence
Train computer to understand not just features within a pixel (spectral) or clusters of pixels (texture, entropy, etc), but context of pixel in space (and over time)
BVLC Caffe Model Zoo has ‘encyclopedia’ of resources ranging including Flickr and Google training data
OmniEarth has developed special classifiers as a function of imagery resolution to handle geometry of nadir- viewing satellite imagery
Wood
Shake
Asphalt Shingles
Tile

11. Using AI for Building Detection1
Label data with desired features: ‘What are buildings?’ 2
Many different types of buildings: varies w/ region, country, population density. Common feature set established. 3
Neural Network Feature Set
Multispectral Image
Neural Network
Building Footprints
@ 1-m Resolution

12. Automated Feature Discovery Index
Change Detection on the Zaatari Camp in Jordan – AFDI Evaluates Temporal Change Independent of Spatial Resolution

13. Earth Observation
Agriculture and Water Management Products Demonstrate Excellent Ability to Quickly Analyze Earth-Based Features and Change
RGB imagery (left) and vegetation index change (right) of Palmyra, Syria from August to September 2015

14. Get In Touch Lars Dyrud, Ph.D. President & CEO OmniEarth, LLC
251 18th Street South – Suite 650
Arlington, VA
Office: (888)
Mobile: (617)