SlideShare a Scribd company logo
S AT E L L I T E I M A G E RY F O R T R A C K I N G
E C O N O M I C C H A N G E
R i s h a b h S r i v a s t a v a | L o k i . a i | @ r i s h d o t b l o g
O V E R V I E W
A. Why use satellite data
B. How to use satellite data
C. Drawbacks and limitations of using satellite data to
estimate growth
A . W H Y U S E S AT E L L I T E D ATA ?
W E S T E R N D E L H I , I N D I A
U s e - C a s e
• Todd, a financial analyst
specialising in the
construction industry
• Wants to identify fast
growing cities (in terms of
infrastructure) in Asia
O b j e c t i v e
E s t i m a t e re l a t i v e i n f r a s t r u c t u re g ro w t h r a t e s
a c ro s s p ro v i n c e s i n A s i a
C h a l l e n g e s
1. Few “official” metrics for measuring granular growth
2. The ones that do exist (like province GDP) often
cannot be compared across countries
3. Even when available, official figures are unreliable
4. Figures in developing countries can come with a
massive (up to 18 months) time-lag
S o l u t i o n c h a r a c t e r i s t i c s
1. Reliable and impervious to manipulation
2. Regularly updated
3. Relatively inexpensive or free
S o l u t i o n :
S a t e l l i t e
I m a g e r y !
• Free sources:
• NOAA Nightlight Data
(updated daily)
• Sentinel-2 Data
(updated every 5 days)
• Paid sources (not covered
in this talk)
B . H O W T O U S E S AT E L L I T E D ATA ?
N I G H T L I G H T
D ATA
• An effective proxy for
economic growth in
developing countries
• Produced by NOAA (USA)
• Updated daily, free to use
A c c e s s i n g t h e
D a t a
• Data available as raster (GeoTiff)
files, where each pixel
represents the brightness (in
nanoWatts/cm2/sr) of a roughly
450m x 450m area
• Files can be read by Python
(with GDAL doing the heavy
lifting), using the rasterio library
• The rasterstats library can be
used to calculate summary
statistics for different areas in a
shapefile
rasterio
rasterstats
C o m p u t i n g
s u m m a r y s t a t s
• You can convert a raster to
a CSV file with average
luminosity in predefined
areas using literally 10 lines
of code
• Shapefile (which is a
machine readable map) of
a region needed for this
E x a m p l e f o r S i n g a p o re
Water reservoirs
Central
Business
District
M e a s u r i n g c h a n g e s o v e r
t i m e
• CSVs generated for each
time-unit can be used to
track changes in average
nightlight intensity over
time
• Significant seasonal
variations can exist
• Interactive version for
Indian districts: https://
embed.loki.ai/nightlight/
G ro w t h o f n i g h t l i g h t i n t e n s i t y i n
A s i a f ro m 2 0 1 3 - 2 0 1 8
I N T E R A C T I V E V E R S I O N : H T T P S : / / E M B E D . L O K I . A I / N I G H T L I G H T / A S I A . H T M L
S E N T I N E L - 2
S AT E L L I T E
I M A G E RY
• Measures reflected solar
radiation across 13-bands of
wavelength (3 of which are
RGB)
• 1 pixel represents 10m x 10m
• Produced by the European
Space Agency
• Updated roughly every 5
days, free to use
A c c e s s i n g t h e
D a t a
• Rasters can be
downloaded from the
ESA’s Sentinelhub, or from
a Google Cloud Storage /
Amazon S3 bucket
D i ff e re n t b a n d s c a p t u re d i ff e re n t
w a v e l e n g t h s o f l i g h t
Sentinel-2 Bands
Central
Wavelength (Nm)
Resolution
(M)
Band 1 - Coastal Aerosol 443 60
Band 2 - Blue 490 10
Band 3 - Green 560 10
Band 4 - Red 665 10
Band 5 - Vegetation Red Edge 705 20
Band 6 - Vegetation Red Edge 740 20
Band 7 - Vegetation Red Edge 783 20
Band 8 - Near Infra-Red 842 10
Band 8A - Vegetation Red Edge 865 20
Band 9 - Water Vapour 945 60
Band 10 - Short-Wave Infra Red 1375 60
Band 11 - Short-Wave Infra Red 1610 20
Band 12 - Short-Wave Infra Red 2190 20
P ro c e s s i n g t h e d a t a
• Different bands can be combined to get meaningful
metrics — like amount of built-up area in a city, amount
of green space etc
• For instance, roads and buildings tend to absorb more
infra-red light (heat) and reflect more visible light
(specially in the blue spectrum)
• A simple, “built-up” index that is commonly used is
(Blue-NIR)/(Blue+NIR). Note: areas with water tend to
also have a high built-up index
Tr u e - C o l o r i m a g e o n l e f t , b u i l t - u p i n d e x b a s e d
i m a g e o n r i g h t ( d a r k e r = m o re b u i l t - u p a re a )
B u i l t - u p a re a b y re g i o n i n S i n g a p o re
( d a r k e r a re a s = m o re b u i l t u p )
Q u a n t i f y i n g
c h a n g e o v e r t i m e
• Changes in built-up
indices can be computed
over time. Extremely
effective at the micro-scale
C h a n g e i n H o C h i M i n h C i t y ’s D i s t r i c t 2 o v e r t i m e
( 2 0 1 9 o n l e f t , 2 0 1 6 i n c e n t re , c h a n g e - m a p o n r i g h t )
C h a n g e i n H o C h i M i n h C i t y ’s D i s t r i c t 2 o v e r t i m e
( 2 0 1 9 o n l e f t , 2 0 1 6 i n c e n t re , c h a n g e - m a p o n r i g h t )
C h a n g e i n H o C h i M i n h C i t y ’s D i s t r i c t
2 o v e r t i m e
Feb, 2019 Jan, 2016
Change Map
(Red= more built)
C . L I M I TAT I O N S A N D C AV E AT S
C L O U D S O V E R S I N G A P O R E
K e y
D r a w b a c k s
• Cloud-cover makes using
satellite imagery far more
difficult
• Extreme haze in India and
China can cause nightlight
and index-based approaches
to go awry
• Algorithm can sometimes
think that water-logged
neighbourhoods are highly
built-up
L i m i t a t i o n s
• Growth due to non-
infrastructure factors (like
Finance or Software) not
measured through satellites
• Assumes that increase in
supply of infrastructure,
nightlights, and buildings =
growth. Not always the case
(Japanese Roads to
Nowhere)
R i s h a b h S r i v a s t a v a
r i s h a b h @ l o k i . a i | l o k i . a i
@ r i s h d o t b l o g
Questions?

More Related Content

What's hot

Who will shape tomorrows cities odw19
Who will shape tomorrows cities odw19Who will shape tomorrows cities odw19
Who will shape tomorrows cities odw19
Dana Chermesh Reshef
 
Introduction to Digital Maps
Introduction to Digital MapsIntroduction to Digital Maps
Introduction to Digital Maps
Paperless Trail Inc.
 
Chris Spry
Chris SpryChris Spry
Chris Spry
Wuzzy13
 
Chris Spry
Chris SpryChris Spry
Chris Spry
Wuzzy13
 
FME and the BGS in 2016/2017
FME and the BGS in 2016/2017FME and the BGS in 2016/2017
FME and the BGS in 2016/2017
Sterling Geo
 
A vision to make OSM data the backbone of history across time and space - Int...
A vision to make OSM data the backbone of history across time and space - Int...A vision to make OSM data the backbone of history across time and space - Int...
A vision to make OSM data the backbone of history across time and space - Int...
Kohei Otsuka
 
calculating wind speed and direction using arcgis
calculating wind speed and direction using arcgiscalculating wind speed and direction using arcgis
calculating wind speed and direction using arcgis
saqibjavaid17
 
CSpryFinal5
CSpryFinal5CSpryFinal5
CSpryFinal5
Wuzzy13
 
Interop conference - Sam Ramji, VP Product Management
Interop conference - Sam Ramji, VP Product ManagementInterop conference - Sam Ramji, VP Product Management
Interop conference - Sam Ramji, VP Product Management
Google Cloud
 
Chris Spry
Chris SpryChris Spry
Chris Spry
Wuzzy13
 
Combat cmems ga_se_h2020_slider
Combat cmems ga_se_h2020_sliderCombat cmems ga_se_h2020_slider
Combat cmems ga_se_h2020_slider
Mercator Ocean International
 
Using OpenStreetMap Building Footprints Data for Population Distribution Mode...
Using OpenStreetMap Building Footprints Data for Population Distribution Mode...Using OpenStreetMap Building Footprints Data for Population Distribution Mode...
Using OpenStreetMap Building Footprints Data for Population Distribution Mode...
Feye Andal
 

What's hot (12)

Who will shape tomorrows cities odw19
Who will shape tomorrows cities odw19Who will shape tomorrows cities odw19
Who will shape tomorrows cities odw19
 
Introduction to Digital Maps
Introduction to Digital MapsIntroduction to Digital Maps
Introduction to Digital Maps
 
Chris Spry
Chris SpryChris Spry
Chris Spry
 
Chris Spry
Chris SpryChris Spry
Chris Spry
 
FME and the BGS in 2016/2017
FME and the BGS in 2016/2017FME and the BGS in 2016/2017
FME and the BGS in 2016/2017
 
A vision to make OSM data the backbone of history across time and space - Int...
A vision to make OSM data the backbone of history across time and space - Int...A vision to make OSM data the backbone of history across time and space - Int...
A vision to make OSM data the backbone of history across time and space - Int...
 
calculating wind speed and direction using arcgis
calculating wind speed and direction using arcgiscalculating wind speed and direction using arcgis
calculating wind speed and direction using arcgis
 
CSpryFinal5
CSpryFinal5CSpryFinal5
CSpryFinal5
 
Interop conference - Sam Ramji, VP Product Management
Interop conference - Sam Ramji, VP Product ManagementInterop conference - Sam Ramji, VP Product Management
Interop conference - Sam Ramji, VP Product Management
 
Chris Spry
Chris SpryChris Spry
Chris Spry
 
Combat cmems ga_se_h2020_slider
Combat cmems ga_se_h2020_sliderCombat cmems ga_se_h2020_slider
Combat cmems ga_se_h2020_slider
 
Using OpenStreetMap Building Footprints Data for Population Distribution Mode...
Using OpenStreetMap Building Footprints Data for Population Distribution Mode...Using OpenStreetMap Building Footprints Data for Population Distribution Mode...
Using OpenStreetMap Building Footprints Data for Population Distribution Mode...
 

Similar to Using satellite imagery to track economic change

Wireless Past Present Future
Wireless Past Present FutureWireless Past Present Future
Wireless Past Present Future
University of Hertfordshire
 
Enel linked open geo data
Enel linked open geo dataEnel linked open geo data
Enel linked open geo data
Raffaele Cirullo
 
Linked Open GeoData for Enel Drive (W3C LOD2014)
Linked Open GeoData for Enel Drive (W3C LOD2014)Linked Open GeoData for Enel Drive (W3C LOD2014)
Linked Open GeoData for Enel Drive (W3C LOD2014)
Andrea Volpini
 
Linked Open GeoData for Electric Vehicle Charging Stations by ENEL
Linked Open GeoData for Electric Vehicle Charging Stations by ENELLinked Open GeoData for Electric Vehicle Charging Stations by ENEL
Linked Open GeoData for Electric Vehicle Charging Stations by ENEL
Redlink GmbH
 
Customer_Testimonial_IFFCO.pdf
Customer_Testimonial_IFFCO.pdfCustomer_Testimonial_IFFCO.pdf
Customer_Testimonial_IFFCO.pdf
PRASHANTJUNNARKAR
 
Plenary Talk from GeCoWest ~ Best of Breed for Geospatial
Plenary Talk from GeCoWest ~ Best of Breed for GeospatialPlenary Talk from GeCoWest ~ Best of Breed for Geospatial
Plenary Talk from GeCoWest ~ Best of Breed for Geospatial
Michael Terner
 
The future is up in the sky
The future is up in the skyThe future is up in the sky
The future is up in the sky
APNIC
 
GOSH! at CERN 2016
GOSH! at CERN 2016GOSH! at CERN 2016
GOSH! at CERN 2016
Safecast
 
Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...
Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...
Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...
Michele Mostarda
 
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...
Massimiliano Crosato
 
F1 A Story of Regional Planning
F1 A Story of Regional PlanningF1 A Story of Regional Planning
Applying Machine Learning to Open Data Sets to find new customers
Applying Machine Learning to Open Data Sets to find new customersApplying Machine Learning to Open Data Sets to find new customers
Applying Machine Learning to Open Data Sets to find new customers
David Stier
 
【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方
【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方
【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方
double jump.tokyo, inc
 
The Shocking Impact of Boring Energy Policy - CommonBound 2016 Conference
The Shocking Impact of Boring Energy Policy - CommonBound 2016 ConferenceThe Shocking Impact of Boring Energy Policy - CommonBound 2016 Conference
The Shocking Impact of Boring Energy Policy - CommonBound 2016 Conference
John Farrell
 
IRJET- GIS using Zigbee
IRJET- GIS using ZigbeeIRJET- GIS using Zigbee
IRJET- GIS using Zigbee
IRJET Journal
 
Aaron ITE Journal Article - Published Jan 2015
Aaron ITE Journal Article - Published Jan 2015Aaron ITE Journal Article - Published Jan 2015
Aaron ITE Journal Article - Published Jan 2015Aaron Zimmerman, PTP
 
04_Penfield_Data_Collection_Book
04_Penfield_Data_Collection_Book04_Penfield_Data_Collection_Book
04_Penfield_Data_Collection_BookKenneth J Meding
 
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...
Piyush Yadav
 

Similar to Using satellite imagery to track economic change (20)

Wireless Past Present Future
Wireless Past Present FutureWireless Past Present Future
Wireless Past Present Future
 
Enel linked open geo data
Enel linked open geo dataEnel linked open geo data
Enel linked open geo data
 
Linked Open GeoData for Enel Drive (W3C LOD2014)
Linked Open GeoData for Enel Drive (W3C LOD2014)Linked Open GeoData for Enel Drive (W3C LOD2014)
Linked Open GeoData for Enel Drive (W3C LOD2014)
 
Linked Open GeoData for Electric Vehicle Charging Stations by ENEL
Linked Open GeoData for Electric Vehicle Charging Stations by ENELLinked Open GeoData for Electric Vehicle Charging Stations by ENEL
Linked Open GeoData for Electric Vehicle Charging Stations by ENEL
 
Customer_Testimonial_IFFCO.pdf
Customer_Testimonial_IFFCO.pdfCustomer_Testimonial_IFFCO.pdf
Customer_Testimonial_IFFCO.pdf
 
Plenary Talk from GeCoWest ~ Best of Breed for Geospatial
Plenary Talk from GeCoWest ~ Best of Breed for GeospatialPlenary Talk from GeCoWest ~ Best of Breed for Geospatial
Plenary Talk from GeCoWest ~ Best of Breed for Geospatial
 
The future is up in the sky
The future is up in the skyThe future is up in the sky
The future is up in the sky
 
GOSH! at CERN 2016
GOSH! at CERN 2016GOSH! at CERN 2016
GOSH! at CERN 2016
 
Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...
Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...
Hydro Mining - Mining Bitcoin and other Cryptocurrencies with hydroelectric p...
 
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti d...
 
F1 A Story of Regional Planning
F1 A Story of Regional PlanningF1 A Story of Regional Planning
F1 A Story of Regional Planning
 
Applying Machine Learning to Open Data Sets to find new customers
Applying Machine Learning to Open Data Sets to find new customersApplying Machine Learning to Open Data Sets to find new customers
Applying Machine Learning to Open Data Sets to find new customers
 
Ica bada
Ica badaIca bada
Ica bada
 
【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方
【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方
【TECH×GAME COLLEGE#22】マイクリプトヒーローズの作り方
 
The Shocking Impact of Boring Energy Policy - CommonBound 2016 Conference
The Shocking Impact of Boring Energy Policy - CommonBound 2016 ConferenceThe Shocking Impact of Boring Energy Policy - CommonBound 2016 Conference
The Shocking Impact of Boring Energy Policy - CommonBound 2016 Conference
 
IRJET- GIS using Zigbee
IRJET- GIS using ZigbeeIRJET- GIS using Zigbee
IRJET- GIS using Zigbee
 
Aaron ITE Journal Article - Published Jan 2015
Aaron ITE Journal Article - Published Jan 2015Aaron ITE Journal Article - Published Jan 2015
Aaron ITE Journal Article - Published Jan 2015
 
RLT - Work Samples
RLT - Work SamplesRLT - Work Samples
RLT - Work Samples
 
04_Penfield_Data_Collection_Book
04_Penfield_Data_Collection_Book04_Penfield_Data_Collection_Book
04_Penfield_Data_Collection_Book
 
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...
 

Recently uploaded

一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
ahzuo
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
mbawufebxi
 
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.
rwarrenll
 
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
axoqas
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
AbhimanyuSinha9
 
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
vcaxypu
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
ewymefz
 
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
ewymefz
 
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationPredicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Boston Institute of Analytics
 
FP Growth Algorithm and its Applications
FP Growth Algorithm and its ApplicationsFP Growth Algorithm and its Applications
FP Growth Algorithm and its Applications
MaleehaSheikh2
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP
 
一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单
enxupq
 
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
NABLAS株式会社
 
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
ukgaet
 
一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单
ewymefz
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
nscud
 
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
74nqk8xf
 
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
dwreak4tg
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP
 
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
slg6lamcq
 

Recently uploaded (20)

一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
 
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.
 
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
 
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
 
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单
 
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationPredicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
 
FP Growth Algorithm and its Applications
FP Growth Algorithm and its ApplicationsFP Growth Algorithm and its Applications
FP Growth Algorithm and its Applications
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
 
一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单一比一原版(QU毕业证)皇后大学毕业证成绩单
一比一原版(QU毕业证)皇后大学毕业证成绩单
 
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
 
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
 
一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单一比一原版(BU毕业证)波士顿大学毕业证成绩单
一比一原版(BU毕业证)波士顿大学毕业证成绩单
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
 
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
 
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
 
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
 

Using satellite imagery to track economic change

  • 1. S AT E L L I T E I M A G E RY F O R T R A C K I N G E C O N O M I C C H A N G E R i s h a b h S r i v a s t a v a | L o k i . a i | @ r i s h d o t b l o g
  • 2. O V E R V I E W A. Why use satellite data B. How to use satellite data C. Drawbacks and limitations of using satellite data to estimate growth
  • 3. A . W H Y U S E S AT E L L I T E D ATA ? W E S T E R N D E L H I , I N D I A
  • 4. U s e - C a s e • Todd, a financial analyst specialising in the construction industry • Wants to identify fast growing cities (in terms of infrastructure) in Asia
  • 5. O b j e c t i v e E s t i m a t e re l a t i v e i n f r a s t r u c t u re g ro w t h r a t e s a c ro s s p ro v i n c e s i n A s i a
  • 6. C h a l l e n g e s 1. Few “official” metrics for measuring granular growth 2. The ones that do exist (like province GDP) often cannot be compared across countries 3. Even when available, official figures are unreliable 4. Figures in developing countries can come with a massive (up to 18 months) time-lag
  • 7. S o l u t i o n c h a r a c t e r i s t i c s 1. Reliable and impervious to manipulation 2. Regularly updated 3. Relatively inexpensive or free
  • 8. S o l u t i o n : S a t e l l i t e I m a g e r y ! • Free sources: • NOAA Nightlight Data (updated daily) • Sentinel-2 Data (updated every 5 days) • Paid sources (not covered in this talk)
  • 9. B . H O W T O U S E S AT E L L I T E D ATA ?
  • 10. N I G H T L I G H T D ATA • An effective proxy for economic growth in developing countries • Produced by NOAA (USA) • Updated daily, free to use
  • 11. A c c e s s i n g t h e D a t a • Data available as raster (GeoTiff) files, where each pixel represents the brightness (in nanoWatts/cm2/sr) of a roughly 450m x 450m area • Files can be read by Python (with GDAL doing the heavy lifting), using the rasterio library • The rasterstats library can be used to calculate summary statistics for different areas in a shapefile rasterio rasterstats
  • 12. C o m p u t i n g s u m m a r y s t a t s • You can convert a raster to a CSV file with average luminosity in predefined areas using literally 10 lines of code • Shapefile (which is a machine readable map) of a region needed for this
  • 13. E x a m p l e f o r S i n g a p o re Water reservoirs Central Business District
  • 14. M e a s u r i n g c h a n g e s o v e r t i m e • CSVs generated for each time-unit can be used to track changes in average nightlight intensity over time • Significant seasonal variations can exist • Interactive version for Indian districts: https:// embed.loki.ai/nightlight/
  • 15. G ro w t h o f n i g h t l i g h t i n t e n s i t y i n A s i a f ro m 2 0 1 3 - 2 0 1 8 I N T E R A C T I V E V E R S I O N : H T T P S : / / E M B E D . L O K I . A I / N I G H T L I G H T / A S I A . H T M L
  • 16. S E N T I N E L - 2 S AT E L L I T E I M A G E RY • Measures reflected solar radiation across 13-bands of wavelength (3 of which are RGB) • 1 pixel represents 10m x 10m • Produced by the European Space Agency • Updated roughly every 5 days, free to use
  • 17. A c c e s s i n g t h e D a t a • Rasters can be downloaded from the ESA’s Sentinelhub, or from a Google Cloud Storage / Amazon S3 bucket
  • 18. D i ff e re n t b a n d s c a p t u re d i ff e re n t w a v e l e n g t h s o f l i g h t Sentinel-2 Bands Central Wavelength (Nm) Resolution (M) Band 1 - Coastal Aerosol 443 60 Band 2 - Blue 490 10 Band 3 - Green 560 10 Band 4 - Red 665 10 Band 5 - Vegetation Red Edge 705 20 Band 6 - Vegetation Red Edge 740 20 Band 7 - Vegetation Red Edge 783 20 Band 8 - Near Infra-Red 842 10 Band 8A - Vegetation Red Edge 865 20 Band 9 - Water Vapour 945 60 Band 10 - Short-Wave Infra Red 1375 60 Band 11 - Short-Wave Infra Red 1610 20 Band 12 - Short-Wave Infra Red 2190 20
  • 19. P ro c e s s i n g t h e d a t a • Different bands can be combined to get meaningful metrics — like amount of built-up area in a city, amount of green space etc • For instance, roads and buildings tend to absorb more infra-red light (heat) and reflect more visible light (specially in the blue spectrum) • A simple, “built-up” index that is commonly used is (Blue-NIR)/(Blue+NIR). Note: areas with water tend to also have a high built-up index
  • 20. Tr u e - C o l o r i m a g e o n l e f t , b u i l t - u p i n d e x b a s e d i m a g e o n r i g h t ( d a r k e r = m o re b u i l t - u p a re a )
  • 21. B u i l t - u p a re a b y re g i o n i n S i n g a p o re ( d a r k e r a re a s = m o re b u i l t u p )
  • 22. Q u a n t i f y i n g c h a n g e o v e r t i m e • Changes in built-up indices can be computed over time. Extremely effective at the micro-scale
  • 23. C h a n g e i n H o C h i M i n h C i t y ’s D i s t r i c t 2 o v e r t i m e ( 2 0 1 9 o n l e f t , 2 0 1 6 i n c e n t re , c h a n g e - m a p o n r i g h t )
  • 24. C h a n g e i n H o C h i M i n h C i t y ’s D i s t r i c t 2 o v e r t i m e ( 2 0 1 9 o n l e f t , 2 0 1 6 i n c e n t re , c h a n g e - m a p o n r i g h t )
  • 25. C h a n g e i n H o C h i M i n h C i t y ’s D i s t r i c t 2 o v e r t i m e Feb, 2019 Jan, 2016 Change Map (Red= more built)
  • 26. C . L I M I TAT I O N S A N D C AV E AT S C L O U D S O V E R S I N G A P O R E
  • 27. K e y D r a w b a c k s • Cloud-cover makes using satellite imagery far more difficult • Extreme haze in India and China can cause nightlight and index-based approaches to go awry • Algorithm can sometimes think that water-logged neighbourhoods are highly built-up
  • 28. L i m i t a t i o n s • Growth due to non- infrastructure factors (like Finance or Software) not measured through satellites • Assumes that increase in supply of infrastructure, nightlights, and buildings = growth. Not always the case (Japanese Roads to Nowhere)
  • 29. R i s h a b h S r i v a s t a v a r i s h a b h @ l o k i . a i | l o k i . a i @ r i s h d o t b l o g Questions?