QuantCube Technology uses artificial intelligence and big data analytics to deliver real-time macro-economic insights. The firm operates one of the largest alternative data lakes in the world, processing more than 14 billion data end points. Sources encompass news, social media, satellite data, professional networks and consumer reviews, as well as international trade, shipping, real-estate, hospitality and telecoms data. During this session we will see how QuantCube Technology uses satellite data to create standardized indicators of economic activity.

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QuantCube Technology ©2021 All rights reserved.
QuantCube
Big Data Analytics for Economic & Financial
Intelligence

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QUANTCUBE DATALAKE
Leveraging on the asymmetry of information to create competitive edges
Big Data and AI
• One of the most sophisticated AI technology
and Big Data processing expertise
• Fully transparent methodology
• Specifically tailored for Financial Markets
Real-time Macroeconomic
• Leading indicator by construction with high
correlation
• Supported by a team of 50+ data scientists
• Based on more than 14 billion datapoints
Our partners

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QUANTCUBE
DATALAKE
QUANTCUBE
ARTIFICIAL
INTELLIGENCE
QUANTCUBE ECONOMIC
INTELLIGENCE FACTORY
Smart Data Catalog
GLOBAL MACRO NOWCAST SMART DATA REAL-TIME SECTOR TRACKERS
Economic Growth (G7 & China)
Inflation Index by components
Sector Employment Index
Business Cycle Index
Credit Cycle Index
Crude Oil Sentiment Index
Iron Ore Trade Index
Automotive Exports Index
Outbound Chinese Tourist Index
Prediction of BDI Index
USE CASES
EXAMPLES
FOREIGN EXCHANGE
ARBITRAGE
MACROECONOMIC
RESEARCH
TACTICAL ASSET
ALLOCATION
CORPORATE
INVESTMENTS
EQUITY OPPORTUNITIES
S&P500 INDEX, ETFs
FIXED INCOME
RESEARCH
PROJECT FINANCING
TRACKERS
REAL ESTATE
INVESTMENTS
OUR PRODUCT: ECONOMIC INTELLIGENCE PLATFORM
NLP
for Text Analytics
Deep Learning
for Pictures Analytics
Graph Theory
for Network Modeling
Machine Learning on
Structured data
Social Media
Professional
Networks
Satellite
Imagery
Blogs
Transportatio
n Data
Real Estate
Hospitality
Consumer
Reviews

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See what Wall Street does not see (feedback)
May 7, 2020
Provides unique real-time macro data
USA, Eurozone, China
Investment Strategies
Unique Proprietary Datalake
GDP Nowcast – US, Eurozone, China
International Trade, Employment, Industrial
Production, Consumer Spendings, …
Maritime, Air & Road Traffic
Multi-lingual Social Media
Many other data sources…
Use cases
BIG DATA ANALYTICS FOR MACROECONOMICS

5. QUANTCUBE SATELLITE DATA ANALYTICS
Use Case: Urban Growth proxy

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The spatial resolution of a satellite image is the
size of the area covered by a pixel.
Each pixel of the image corresponds to part of the
surface of Earth.
QUANTCUBE SATELLITE DATA ANALYTICS
Introduction: Spatial Resolution

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Mining Agriculture Manufacturing Oil&Gas Industry etc…
q Earth Observation satellites : Sentinel 2 & Landsat
7&8
q High Resolution satellites : Pléiades (50cm de
résolution)
q Atmospheric satellites : Sentinel5P & OCO-2
q Radar satellites : Sentinel 1
q Thermal images : Landsat 8 & Sentinel 3
MACRO ECONOMIC LEVEL
SECTOR ANALYTICS
Urban Growth Air Pollution Water Stress
QUANTCUBE SATELLITE DATA ANALYTICS
Access to any satellite data through our partnership with European Space Agency

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Pleiades satellite image
Deep neural
network
Number of vehicles in each region of interest
Commercial sites
Industrial sites
Vehicle assembly sites
Construction sites
Streets
50 cm resolution
Is car detection possible at
this resolution ?
USE CASE: Car detection in satellite images
Technical specification of the solution

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● Images from satellites Pleiades 1A &
1B
● 50cm spatial resolution
● 4 bandes : RGB and NIR
● Geographic area : region of Paris, France
● Heterogeneous environments : rural, forest,
residential and industrial areas
USE CASE: Car detection in satellite images
Pleiades images description

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Three different strategies :
1. Semi-supervised labeling using a network trained on a public aerial image dataset adapted to the
resolution of Pleiades sensor;
2. Constitution of training data by integrating synthesized images;
3. Semi-automatic annotation of Pleiades images.
Aerial images of 5 cm
spatial resolution
downscaled at 50 cm :
● colorful vehicles
● sharp outlines
Pleiades images :
● white or black
vehicles
● irregular outlines
● indistinguishable
shadows and cars
USE CASE: Car detection in satellite images
Semi-automatic annotation process - Creation of our training data set

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● An interactive labeling tool was developed
● Enables the labeling of 60% of vehicles with one click using flood-fill methods adapted for this
application.
● The mask color is automatically changed in order to distinguish two touching cars. Next, all
bounding boxes are extracted.
87 000 labelled vehicles internally
USE CASE: Car detection in satellite images
Semi-automatic annotation process - Creation of our training data set

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We investigated a segmentation algorithm Tiramisu [1] with post-processing and we adapted a
direct detection network YOLOv3 [2].
Vehicle detection from satellite images is a particular case of object detection as :
● Objects do not overlap
● Objects are uniform
● Objects are very small (around 5*8 pixels/vehicles in Pleiades images)
[1] Tiramisu: “The One Hundred Layers Tiramisu : Fully Convolutional DenseNets for semantic segmentation”, S.
Jégou, M. Drozdzal, D. Vazquez, A. Romero et Y. Bengio (2017)
[2] YOLOv3: “An Incremental Improvement”, J. Redmon et A. Farhadi (2018)
USE CASE: Car detection in satellite images
Modelling - Deep learning models for vehicle detection and counting

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Data Augmentation added at the
predicting phase with padding and
geometric operations.
Post-processing to count
vehicles based on the size and
shape of the predictions within the
segmentation mask
20 predictions per pixel that are
processed by a voting system.
The number of pixels associated
with a block of multiple cars is
divided by the mean number of
pixels observed either in lined cars
or in side by side cars.
USE CASE: Car detection in satellite images
Modelling – Tiramisu – Segmentation Model

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● Removing two detection levels related to large
objects (with a sub-sampling factor called stride of
32 and 16)
● Replacing them with two new prediction levels with
strides of 4 and 2.
State of the art detection neural network that was adapted to very small objects detection
USE CASE: Car detection in satellite images
Modelling – YOLOv3 – Detection Model

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Conclusion : Pleiades satellite images are exploitable for vehicle detection and counting applications.
Detection results from a validation set
including 2673 vehicles.
Results using Tiramisu (middle) and YOLO model (right).
Color codes: green for good detection, blue for missed detection and red for false alarms
USE CASE: Car detection in satellite images
Results – Qualitative and Quantitative evaluations

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There are vehicles detected by YOLO but not by Tiramisu and vice
versa.
➔ A mixed model based on both predictions may increase the
number of detected vehicles.
Mixed model :
- Recall = 80.3%
- Precision = 81.8%
Tiramisu YOLO Mixed model
USE CASE: Car detection in satellite images
Results – Qualitative and Quantitative evaluations

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32
8
47
4
170
17
6
5
14
12
13
20
→ Get the number of vehicles per ROI
→ Aggregation per sector (Macro Smart Data) or company (Equity Smart Data)
Hospitality
Commercial
Logistics
Number of vehicles
60
USE CASE: Car detection in satellite images
Tracking activity level by tracking vehicles

18. New York (USA)
60 Broad Street, Suite 3502
New York, NY 10004, USA
Paris (France)
15 Boulevard Poissonnière
75002 Paris, FRANCE
For general enquiry:
info@quant-cube.com
www.quant-cube.com
THANK YOU FOR YOUR ATTENTION !