How AI can Save Lives with the Help of Satellite Imagery
1. How AI Can Save Lives with the Help of
Satellite Imagery
Ganes Kesari
Nov 2, 2020
2. 2
THE MYSTERY OF THE MISSING KID
Sources: News - CBS News | Map picture from People | Google Earth | Movie on IMDB
3. 3
UNEXPECTED APPLICATIONS OF SATELLITE IMAGES
How cities
grow
When nature
declines
How to adopt
solar power
When disaster
strikes
When people
quarantine
5. 5
INTRODUCTION
Ganes Kesari
Co-founder & Chief Decision Scientist
“Simplify Data Science for all”200+ Team100+ Clients
Insights as Stories
Help start, apply and adopt Data Science
@kesaritweets
/gkesari
7. 7
WHAT’S THE MOST DANGEROUS ANIMAL ON OUR PLANET?
Image Source: Infographic - Gates Notes | World map from Wikimedia – by KVDP – Own work, CC BY-SA 3.0
Mosquito-borne diseases: Tropic nightmare
400 million
cases..
..in 100+
countries..
..40% of world
population
Dengue
9. 9
SUCCESS STORY: NORTHERN QUEENSLAND
Source: Success story - WMP website | Paper - Gates Open Research
10. 10
HOW IS THIS DONE?
Scoping Setup Prepare Release Monitoring
Surgically
release
mosquitoes
with precision
Establish
infrastructure
and regulatory
processes
Rear
mosquitoes:
genetic
engineering
Identify
Potential
release sites
in the localities
Monitor the
wild
mosquitoes
over time
11. 11
WHERE TO RELEASE THE MOSQUITOES?
Resource-Intensive
Process
Risk of Missing
Human Settlement Areas
Accuracy of Choosing
Release Sites in Large Cities
Requires Specialist
Teams (GIS, Analytics)
Requires Correction by
Ground-Truthing
People
Issues
Operational
Issues
20. 20
SOLUTION OVERVIEW : 3 STEPS TO DEFEATING DENGUE
20,000 ppl / km2
15,000 ppl / km2
Detect Buildings
Overlay multiple geo-
statistical data layers
Estimate Population
100m2 grids
e.g.
Deep Learning Model to
detect building footprints
Geospatial Analytics to
compute vegetation
Statistical distribution
Population Count
21. 21
STAGE 1: DEEP LEARNING MODEL FOR BUILDING FOOTPRINT EXTRACTION
Phase 1 :
Building Footprints
Phase 2 :
Population (Statistical Distribution of
population data), Land Use & Other
Sources
22. 22
STAGE 2: DETECTING HIDDEN FEATURES IN SATELLITE IMAGES
+ +
=
Green cover Water cover Land cover
Sentinel 2 satellite image-MSI, Sunabeda, Odisha
24. 24
WHAT DO THE REGION DENSITY MAPS LOOK LIKE?
Input Satellite Imagery (50 cm
resolution)
Output Gridded Maps
(50m * 50m)
Micro-
Scale
Low
Population
High
Population
City-
Scale
25. 25
THE SOLUTION: PUTTING IT ALL TOGETHER
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Train Test
Split
Building
Footprints
Gridded
Map
Data Preparation
Grids Building Footprints
Model execution
Population estimation
Region
Boundary
BFP from
Model
GPW
Data
NDVI Mask
Output Imagery with
• Population by Area Coverage and
areas with No Human settlement
• Grid-wise Building area with
Number of Buildings
• Spatial Clustering of Grids
Site Planning Decisions
Area of Interest
identification
26. 26
OUTCOMES: HIGHLIGHTS OF THE SOLUTION
Reduced the time taken from ~3 weeks2 hrs
Accurate release plan with very high ROI70%+
Efficient post-release monitoring & validation50%
1. Effort Savings
2. Better
Effectiveness
3. Higher Efficiency
The solution is being rolled out across countries
Press Release: Defeating Dengue with AI
28. Monitoring Salmon in Rivers Predicting Quality of Life from
Satellite Imagery
Species Classification API
Saving the African Elephant Camera Traps Penguin Counts in Antarctica
GRAMENER WON
MICROSOFT AI
AWARD 2018
Gramener partnered with
Microsoft AI for Earth to
help Nisqually River
Foundation automate the
identification of fish
species using AI-driven
deep learning models.
We’ve been applying AI for Good by solving problems around the world
Some of our work in this area…