Gramener's Lead Data Scientist Soumya Ranjan and Senior Data Science Engineer Sumedh Ghatage conducted a webinar on Geospatial AI. In this webinar, they discussed the technical know-how to get started, as well as some strategies for navigating this fascinating realm of Geospatial Analytics. Pain points covered : -How to begin with Geospatial Analytics in Python -How can large-scale geospatial datasets be cleaned and analyzed? -What is the best way to design geospatial workflows? -How to use Geospatial Datasets for Deep Learning? No matter whatever industry you're in, Geospatial Analytics will provide you with a wealth of unique solutions. To watch the full webinar visit: https://info.gramener.com/geospatial-ai-technical-sneak-peek To know more about Gramener's Geospatial AI solutions book a free demo on: https://gramener.com/demorequest/

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Data People
Hola Personas De Datos
डेटा लोग
你好 數據人
नमस्कार
Hello

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DAWN OF THE GEOSPATIAL AI :
A TECHNICAL SNEAK PEEK
19 NOVEMBER 2020

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INTRODUCTION
Sumedh Ghatage
Geospatial Data Scientist
100+ Clients
@ghatagesumedh
/sumedh-ghatage
Insights as Stories
Help start, apply and adopt Data Science
Soumya Ranjan Mohanty
Lead Data Scientist
@srmsoumya

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AGENDA
• What is Geospatial Data Science?
• Getting Started with GIS and Remote Sensing
• Geospatial Analytics - Python Libraries
• Jupyter and Papermill Workflow
• GEO - AI at Gramener
• What Next?

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Data science is an inter-
disciplinary field
Extract knowledge and
insights from many structural
and unstructured data
Asking right questions to the
data to get right outputs,
visuals, and narrative
What is Data Science?

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What is Geospatial Data Science?
Geospatial Data Science is a
subset of Data Science
Focuses on the unique
characteristics of geospatial
data
Beyond simply looking at
where things happen -->
To understand why they
happen there.

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Reference gislounge.com
Geospatial
Technology
Remote Sensing
GIS
GPS-Survey Digitization Active Passive

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Vector data structures represent specific
features on the Earth’s surface, and assign
attributes to those features.
VECTOR DATA

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A raster consists of a matrix of cells (or
pixels) organized into rows and columns
(or a grid) where each cell contains a value
representing information
Reference earthdatascience.org
RASTER DATA

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UNDERSTANDING SATELLITE IMAGES
Electromagnetic Spectrum True Colour Composite
R,G,B
False Colour Composite
NIR, R, G
Reference www.nrcan.gc.ca

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● Visualize, question, analyze, and
interpret geographical data
COMMONLY USED PLATFORMS
● Edit Data on the fly
● Processing Capabilities
● Multiple Layer Overlays
● Business Intelligence

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● Storing and querying data that
represents objects defined in a
geometric space
COMMONLY USED DATABASE SYSTEMS
● Ability to cope-up with Points,
Lines & Polygons
● Majorly used with Vector
Datasets
● Ease of working with Spatial Big
Data

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PYTHON - GEOSPATIAL LIBRARIES

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GEOFLOW
Geospatial Workflow
using
Python,
Jupyter Notebook &
Papermill

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AUTOMATION USING PYTHON & JUPYTER NOTEBOOKS
Read & display a GeoJSON

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AUTOMATION USING PYTHON & JUPYTER NOTEBOOKS
Clip Raster to a specific region

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AUTOMATION USING PYTHON & JUPYTER NOTEBOOKS
Visualize building footprints over population data

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REPRODUCE USING PAPERMILL
Parameterize Notebooks

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REPEAT USING PAPERMILL
config.json

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GEOFLOW
https://srmsoumya.github.io/blog/

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Predicting Quality of life
Geo AI in Gramener

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WE HAVE MANY SATELLITES TODAY..
Mapping the Earth
Image by Naideh Bremer

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..CAN WE PUT THEM TO BETTER USE?
Satellites are more helpful for
Pizza delivery...
..than for the delivery of
emergency services

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Daytime satellite imagery Night-time satellite imagery
Inspiring work by Stanford on Predicting Poverty
http://sustain.stanford.edu/predicting-poverty

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Model extracts features.. ..and predicts poverty in Africa
Estimate per-capita consumption expenditure
http://sustain.stanford.edu/predicting-poverty

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WE TRIED PREDICTING THE QUALITY OF LIFE FROM SATELLITE IMAGES

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…OUR MODEL PREDICTED WEALTH LEVELS..
Actual vs Predicted Maps on Kepler.gl

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..AND, THE MODEL ESTIMATED THE TYPE OF ROOF!
Actual vs Predicted

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HERE’S A QUICK DEMO OF THE APPLICATION
Demo: https://qol.gramener.com | Built on Streamlit.io

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Saving Lives using AI
Geo AI in Gramener

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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: A health hazard in the tropics
400 million
cases..
..in 100+
countries..
..40% of
world
population
Dengue

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WMP’S INNOVATION: INFECT THE MOSQUITOES!
Image Source: WMP website

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SUCCESS STORY: NORTHERN QUEENSLAND
Source: Success story - WMP website | Paper - Gates Open Research

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THE SOLUTION: PUTTING IT ALL TOGETHER
You can edit this text
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
Identify Region of Interest

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WHAT DO THE REGION DENSITY MAPS LOOK LIKE?
Input
Satellite Imagery (50 cm resolution)
Output
Gridded Maps (50m * 50m)
Micro-Scale
City-Scale
Low
Population
High
Population

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OUTCOMES: HIGHLIGHTS OF THE SOLUTION
Reduced the time taken from ~3 weeks
2 hrs
Accurate release plan with very high ROI
70%
Efficient post-release monitoring & validation
50%
1. Effort Savings
2. Better Effectiveness
3. Higher Efficiency
The solution is being rolled out across countries
Press Release: Defeating Dengue with AI

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Disaster Planning &
Recovery
Geo AI in Gramener

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ASSIGNING FLOOD RISK SCORE
Multicriteria Decision
Making Analytics
Road Networks Building Footprints Digital Elevation Model Slope
Impervious Surfaces Vegetation

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EXPECTED RESULTS
Multicriteria Decision
Making Analytics

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What Next ?
Geo AI in Gramener

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AGRICULTURE
Crop Type Mapping and
Feature Extraction
Crop monitoring and
Damage Assessment
Precision Farming &
Production Control
Crop Insurance and
Management
SMART CITY ANALYTICS
Urban- Natural
Resource Monitoring
Building footprints
Extraction
Accessibility and
Livability Measurement
Land Use - Land Cover
Planning
LOCATION INTELLIGENCE
Price Surge Modelling Geomarketing and
Recommendation Systems
Site Suitability
Analytics
Solid Waste
Management
OTHERS
Disease Outbreak
Monitoring & Control
Forest Fire Hazards and
Mapping
Property Tax
Automation
Election Campaign
Management

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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…

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THINK SPATIALY
Sumedh Ghatage
Geospatial Data Scientist
@ghatagesumedh
/sumedh-ghatage
Insights as Stories
Help start, apply and adopt Data Science
Soumya Ranjan Mohanty
Lead Data Scientist
@srmsoumya
Thank You!