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Listening To The Pulse Of The Planet

Finally, it takes FOREVER to process and get valuable data from the image

EOS, Inc., Proprietary and Confidential

There are 338 active earth observation

satellites in the space

They take more than 100,000 Earth Surface

images daily

The average size of such

an imagery is 300,000 GB

2

THOUSANDS AND THOUSANDS of

commercial drones, UAV and aerial imagery

data sets

Earth Observation Today

Cloud, Highly Scalable EO Analytics

EOS Platform-as-a-Service

3 EOS, Inc., Proprietary and Confidential

EOS Data Warehouse

“Workbench” - rapid solutions developmentWorkflowSteps/processesBusiness RulesGeospatial methods

Neural Networks and Novel Analytics Operations

EOS Data Warehouse

EO Data Agnostic

• Easy data access

• Ability to apply filters for data search

• Ability to store and distribute any kind of data

• Data indexation for a quick search

Structured storage for petabytes of newest and archive data

• Satellites

• Aerial

• UAV

Proliferation of Sensors

• Neural Networks Based Methods

• Point Cloud – Photogrammetry

• Change Detection

• Imagery Tiling and Mosaicking

Unlimited geospatial data storage capabilities

Cloud-based raw and custom data processing for

multipurpose imagery analytics:


• Huge data sets

• Replacing algorithmic methods

• Rapid solutions development

Artificial Intelligence

EOS, Inc. – Proprietary and Confidential

Neural Networks New Image “Operations” Methods

Machine learning (CNN)

Representative:

• Land and species classification

• Feature recognition, extraction

Advancements and benefits


The segmentation and object recognition quality improvements is achieving

using original two-step approach of object recognition: segmentation and initial

classification of segments with following, more detailed, classification and

elaboration of object attributes. We use our own realization of convolutional

neural network with the ability to process multiband aerial imagery

CNN Workflow

1. A breakdown of scene on logical components of the same

size.

2. Feeding of logical component into classification of machine

learned neural network.

3. Identification of object mask based on classified logical

component.

4. Result interpretation based on classified logical components.

PaaS of algorithm sets, methods and technologies for any

type of imagery analytics at any scale.

The powerful set of remote sensing image processing technologies

On-the-fly imagery analytics, tiling and mosaicking

service.

EOS Engine API (B2B) LandViewer (B2C)

Two Products - Two Go-to-Markets Strategies


Massive scale, embedded PaaS Public domain interface to EOS

EOS ENGINE API

(B2B solutions)


Neural Net methods

PaaS – “Engine”

EOS Engine

Real-time analytics processing

Rapid use case configuration• Workflow

• Steps

• Business Rules

• GIS methods

Persistent data updates – results


• Brand

• Customer base

• Sector expertise

• Procurement vehicles

White Label/OEM

EOS Engine Business Model

“Embedded” Industry Leading

Partners – 22 Verticals

Existing industry positions


Problem Statement:• Affecting outdoor water conservation efforts in

urban water districts by establishing target water

consumption rates

Unique Solution:• Applying neural nets to classify and measure

statewide all land cover types with applied

evapotranspiration rates

Customer Benefit:• Equitable and cost effective method to calculate

highly detailed water consumption rates based on

landcover types

Water Conservation

Sensor type: aerial photography

Resolution: 15 cm

Classification performance: ~ 4 min / 1 km2 / class

Vectorization performance: ~ 9 min / km2 1 / Class

Neural Nets - Landcover Classification and Measurement


Problem Statement:• Retail Forecasting Analytics in near real time by

automating and contrasting car density counting

Unique Solution:• Utilizing Artificial Intelligence with high resolution, high

cadence satellite imagery, extracting and counting

features by geofenced retail locations

Customer Benefit:• New and novel approach to provide

futures/commodities brokers new methods for

competitive advantageClassification performance: ~ 2 min / 1 km2

Vectorization performance: ~ 4 min / 1 km2Sensor type: satellite imagery WorldView family

Resolution: 50 cm

Bloomberg Terminal EO Analytics

Neural Network Feature Detection


11

Problem Statement:• Need to accurately and repeatedly monitor crop

production rates for supply chain management

Unique Solution:• Utilizing AI (neural nets) and high-cadence satellite

imagery identify ever changing crop zones and

types production yields

Customer Benefit:• Ability to measure crop production to provide

valuable insights for supply chain customers as an

entirely new product and revenue stream

RELX Group

Sensor type: SAR and optical satellite imageries

Resolution: 50 cm

Classification performance: ~ 2 min / 1 km2

Vectorization performance: ~ 4 min / 1 km2

Neural Network Unsupervised Classification


Problem Statement:• Ability to monitoring at high cadence the production

status of wellheads to forecast supply and pricing

Unique Solution:• Label unique ”signals” (Equipment) of the lifecycle of

well production using AI

Customer Benefit:• First weekly cadence ability to monitor well production

nationwide for accurate production forecastsClassification performance: ~ 2 min / 1 km2

Vectorization performance: ~ 4 min / 1 km2Sensor type: satellite imagery Airbus Pleiades

Resolution: 50 cm

Oil & Gas Wellhead Monitoring



Problem Statement:• Need for macro gross domestic product metrics –

worldwide and at high cadence

Unique Solution:• Utilizing NOAA NPP night light sensors apply change

detection and derive indices of GDP change

Customer Benefit:• First worldwide macro indicator of GDP indices for

financial analytics


Vectorization performance: ~ 4 min / 1 km2Sensor type: satellite imagery NOAA NPP

Resolution: 50 m

GDP Night Lights Indices

NOAA NPP Sensor Change Detection


Problem Statement:• Ability to monitor mining production operations –

worldwide at 5 days cadence for commodities pricing

Unique Solution:• Utilizing Sentinel 1 SAR satellites detect 1cm

elevation change indicating operations rates

Customer Benefit:• Competitive advantage for near real-time production

monitoring for futures pricing


Vectorization performance: ~ 4 min / 1 km2Sensor type: satellite imagery ESA Sentinel 1

Resolution: 1 cm vertically

Mining Operations Monitoring

InSAR Change Detection


Problem Statement:• DOT regulations requiring monitoring of

encroachments upon gas transmission pipelines is

a manual, time-consuming and costly process

Unique Solution:• Utilizing multiple 3D point cloud imagery sets from

aerial photographs (photogrammetry) automatically

detect both change and features of encroachment

Customer Benefit:• Highly accurate machine learning methodology to

decrease the time and cost to ensure public safety

Gas Pipeline Encroachments - HCA

Mesh performance: ~0.5 h/1 km2

Texturization performance: ~4 h/1 km

Sensor type: UAV

Resolution: ~10 cm

Point Cloud Change Detection – OTF Photogrammetry


LandViewer

(B2C solutions)


EO data resell – added value

analytics

Earth Observation BI for the Everyman

LandViewer EO Data and Analytics Discover

On-the-fly, real-time imagery processing

On-the-fly, real-time analytics processing

Simple analytics – freemium

Complex analytics – fee subscription

Everyman’s introduction to EO analytics


Different, preinstalled, and custom spectral

band combinations to highlight and visualize

any data type

On-the-fly indices processing, to view indices

via web browser and download processed

imagery

1st Iteration Analytics

On-the-fly Spectral Indices Processing


Problem Statement:• Never used EO imagery to understand crop health

Unique Solution:• Utilizing aerial imagery and applying on-the-fly

spectral indices analyses identify crop disease,

drought situations, etc

Customer Benefit:• Entirely new and cost effective methods to monitor

crop health and mitigate issue to improve crop yield

Spectral Indices with Aerial Imagery


21

Imagery Providers Marketing and Resell

Problem Statement:• Lack of market presence with valuable product and

opportunity for increased revenue generation

Unique Solution:• Provide to the mass market TerrAvion search and

purchasing services for their imagery products with value

add service (on-the-fly analytics)

Customer Benefit:• Mass market exposure to TerrAvion product with value

added analytics to provide large revenue uplift and PR.


EOS Collaborative Activities


EOS Partner- "Earth on AWS" consortium

• InSAR, Sentinel-1

• Sentinel-2

• Deep Leaning Amazon Machine Instance

• Potential Additions: Sentinel-3, MODIS

11

Whale Strike Avoidance:• seeking to dramatically improve the precision of

“whale maps” by moving beyond simple ecological

models to harness the full array of data that are

available.

Monitoring of coastal hazards in port cities in

Africa:• how diverse (EO) data streams can be integrated

to inform decisions relating to the mitigation of

coastal and marine hazards in a changing climate.

Remote sensing of marine debris• using different remote sensing techniques and

sensors to track plastic and derelict fishing gear

Pilot Projects:

Potential & ongoing Blue Planet Projects


Monitoring of sea ports – Planet imagery


Dynamics of water bodies and shores


Hierarchical controllable classification


EOS, Inc., Proprietary and Confidential29

Monitoring of oil spills prototype – Caspian Sea

Using Sentinel-1 GRDH (InSAR) scenes, Sentinel-2, and weather data

Sea Surface Oil Spill Detection

Dark spots of different nature on Sentinel-1 images:a) oil spill in the Mediterranean Sea;

b) slick structures from methane seeps (the Black Sea); c) calm wind over the Mediterranean Sea


Oil spills in the Mediterranean Sea and buffer zones of 1 km radius


Determination of polluted area, Terra-SAR-1 data from

11/11/2007

Radar image of the Kerch strait (between the

Black Sea and the Sea of Azov)

11/16/2007 гaw

Results of processing.

Area of pollution – 107,5 km2

Quantity of fuel oil spilled into the sea – around 1200

tons (acc. to Russian sources – 1300 t)


www.eos.com facebook.com/eosda/twitter.com/eos_da

EOS, Inc.

1906 El Camino Real

Menlo Park CA 94027

Questions?

[email protected]

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