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

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

” “ 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. “

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

The Problem Processes Are Interrelated But Our Measurement Systems Are Not

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

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

but create terabytes of data daily

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

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

Using AI for Building Detection 1 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

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

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

Get In Touch Lars Dyrud, Ph.D. President & CEO OmniEarth, LLC 251 18th Street South – Suite 650 Arlington, VA 22202 Office: (888) 838-6318 Mobile: (617) 945-4568 Email: lars.dyrud@omniearth.net