Enhance Earth Observation
with Mobile Big Data
Mariko Shibasaki
LocationMind Inc.,
CSIS, The University of Tokyo

Mariko SHIBASAKI (Ms.)••••

An Engineer and A Consultant at LocationMind Inc.

•LocationMind Inc is a Geospatial AI company, a start-up of CSIS, University of Tokyo.

My working experiences include;••

IBM Japan (IT engineer)
LIRNEasia (volunteer as a young research assistant for research on seasonal worker migration. LIRNEaisa
is NGO. Sri Lanka for improving people’s life and support policy-making for human development.)

My interests include Geospatial technology development and applications to;••

Humanitarian assistance andSustainable balance with wildlife/nature environment and human society using technology and a sociallyinclusive approach.

Now Working in GEO Disaster Risk Reduction (DRR) Working Group from 2021

EO Risk Toolkit by GEO Disaster Risk Reduction (DRR) Working Group•••

DRR working group is developing EO Risk Toolkit in collaboration with the UNDRR flagshipinitiative Global Risk Assessment Framework (GRAF) and ESRI.
Satellite imagery plays a major role in the EO Risk toolkit. However, considering the impact ofdisasters on human activity, society, and economy, it is also very important to quickly monitor
human activities.
Mobile big data from mobile phones has great potential and is available in all countries.

Approach to human activity with Mobile Big Data•

Call Detail Records (CDR)
Data on time and location (base station) of
calling/messaging/data communication ofeach hand-set for billing purposes. Data isrecorded only when calling, messaging anddata communication.

data
data
Unique Feature
・Collected by all Mobile
Network Operators.
・Standardized data
・Better data representative

ADB-JAXA-UT Project (Technical Assistance as Proof of Concept)“Flood Warning Service from Space to Mobile Phones” in Bangladesh

Real-time rainfall measurement

Water run-off modelPeople evacuation monitoringwith mobile phone location data
3000

River discharge

計算流量
実績流量

2500

Disaster Prone Areas in Bangladesh(By Districts)

N

2000

GE

1587

Li
B

S

Nst

S

1500

A
T

1694

Aa

A

R.
50T

M
M

E

O

1000

H

G

50 Kilometers

A

A

L

Yl

A

A

dta

B
rruam

S

Srhmi

La
A

R.

p

Nu
GtEG

A

500

S AMaGTra
RSIGNR.
AoPMINDIA

INDIA

r

E

R
U

Ia

7/10:00i

B

06/28 6/28 6/28 6/28 6/29 6/29 6/29 6/29 6/30 6/30 6/30 6/30
0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00 0:00 6:00 12:00 18:00

A

W

0

INDIA

S

INDIA

E

流量（m3/s）

Z
R.

INDIA

O

T

Pa
R

Shu
r

cha
R.

Ka
A

E

Disseminating
Warning Information
through Mobile phones

R.

M

RW

Observation from Satellites

Flood ForecastA

LEG END

B A Y

O F

B E N G A LInternational B oundaryM

District B oundary
River Sys tem sCyclone & Tidal Surge

ANYFloo dFla sh Flo odM

Dr oug ht

Man ag em ent Inf orm ation & Mo nitoring (MI M) D ivisio nDisa st er M an age me nt Bu reau (DM B), Se pte mb er 1 3 '99

St. Martin Is.

Machine Learning-based Anonymized Mobile Phone Data and Satellite Imagery Analysis for
Disaster Vulnerable Population Extraction Modeling: A Case Study in Maputo, Mozambique.

•We developed a model to estimate slums through machine learning, and to analyze the number of vulnerable
populations living in these areas and their actual movements using satellite image and Call Detailed Records.

Slum detection modelusing satellite image・Features for City structure

Calibrated by fieldsurvey

CDR

Aggregation of population
distribution by socio-economic
attribute using CDR・Income level
・Season / Timeslot⇒Socially Vulnerable people
distribution

・Grid data of Disaster
Vulnerable Population

Open-Source Software ・Algorithm／Analysis Tool

Flow of slum detection from Satellite image with machine learning

Detected slum area from our model

Satellite image

Learning with feature of city structure andtraining data from result of field survey-shape and density of building-regularity of road network etc.

Slum area found by field survey
Evaluation our modelMachine Learning-based Anonymized Mobile Phone Data and Satellite Imagery Analysis for
Disaster Vulnerable Population Extraction Modeling: A Case Study in Maputo, Mozambique.

•We developed a model to estimate slums through machine learning, and to analyze the number of vulnerable
populations living in these areas and their actual movements using satellite image and Call Detailed Records.

Slum detection modelusing satellite image・Features for City structure

Calibrated by fieldsurvey

CDR

Aggregation of population
distribution by socio-economic
attribute using CDR・Income level
・Season / Timeslot⇒Socially Vulnerable people
distribution

・Grid data of Disaster
Vulnerable Population

Open-Source Software ・Algorithm／Analysis Tool

Disaster Vulnerable Population Extraction from CDR◼ Mobile Phone ownership rate
→68% of household◼ Mobile Phone sharing ownershiprate→1% of household

Different distribution in Different income group
Low-income group

Population distribution in Dry season

Lower-middle income group

In daytime

Innighttime

In daytime(Left)

In nighttime(Right)

Future Direction••

Integrate satellite-based Earth observation with data on human activities tobetter quantify the impact of human activities on the environment and local
socio-economy.

Summarize the results of GEO’s activities using natural language
processing(NLP) and AI for use by experts in different and varied fields.