The document discusses an all-hands lunch session focused on applied machine learning (ML) and a personal project utilizing cloud APIs for analyzing satellite images. It covers the fundamentals of ML, the technologies used, and real-world applications, including population growth studies and mapping poverty hotspots. The presentation highlights potential future applications of ML at NASA, including modeling and analyzing satellite data to gain insights into human activities and ecological patterns.

1. I N T R O
All-hands Knowledge-sharing Lunch!
• This month's session is about Applied Machine Learning (ML) - a test
personal project I am working on, the reasons thereof, and the technology
underneath.
• The project uses APIs from Cloud vendors to sift through satellite images.
• The goal today is to start a discussion around Emerging Tech at NASA.
Applied ML - Harsh Prakash 1

2. Applied ML - Harsh Prakash 2
W H A T I S M L ?
• ML, a subset of AI and a superset of DL, enables a user to perform
specific tasks, like predicting outcomes and recognizing images, without
explicit instructions by analyzing and learning from data based on
patterns and inference, and with minimal human intervention.
• NLP, a subset of AI, helps a user read, analyze, interpret and understand
natural language data, and perform speech recognition.
• AI helps a user’s computer systems learn (acquire data and the rules
governing its use), reason (reach conclusions), problem-solve and self-
correct to inform its decisions.
• Neural Network is at the heart - Designed to recognize patterns (variables
that rise and fall together).

3. L I V E D E M O *
• Web app on Apache uses AWS SDK for Rekognition API connected to a video
camera for near real-time image analysis.
• ML assigns LABELS/TAGS, and returns raw JSON response from the Model API.
• Can adjust MAXLABELS, MINCONFIDENCE, etc., be ported to Lambda/S3 Bucket,
and send alerts.
* Service currently available in AWS GovCloud (US-West) only.
Applied ML - Harsh Prakash 3

4. Applied ML - Harsh Prakash 4
B E S T U S E ?

5. Applied ML - Harsh Prakash 5
COLLEGE PROJECT *
Growth Study for Charlottesville VA, 2000-2030
Annual Scholarship, 2001
Used satellite images and Census data to compute population growth
distribution –
• Divided study area of the county into 5,745 grid cells (250 meters x 250
meters).
• Traditional compute model assigned growth weights based on development
indicators at the neighborhood level.
* https://www.slideshare.net/gisblog/gis-growth-study-for-charlottesville-va-20002030-plan-885-vamlis-2001-38716260
Development Indicator

6. TEST PROJECT *
• As volunteer Directors, our focus is on mapping poverty hotspots.
• Using Cloud-based ML model with satellite images to detect development
indicators at the neighborhood level.
* https://www.globalmapaid.org/patron-directors/
Applied ML - Harsh Prakash 6

7. STEPS
1. Opened account with Google Cloud Platform (GCP).
2. Enabled Google Maps API for project.
3. Enabled billing for project to fetch more than 1 satellite image per day
using API key.
4. Tuning model for known test areas. E.g. New York...
• Using satellite images for Ethiopia’s capital, Addis Ababa, from Google
Maps API at their highest available resolution (zoom: 17, or 1x1 sq.
mile).
• Using Cloud-based ML model to classify satellite images by infrastructure
levels.
• Assuming correlation between infrastructure and visual indicators in
satellite images.
Applied ML - Harsh Prakash 7

8. Bridge – New York City, NY
ML assigns labels:
Nature, Outdoors, Landscape, Scenery
Applied ML - Harsh Prakash 8
City Center – New York
City, NY
ML assigns labels:
Outdoors, Nature,
Landscape, Scenery,
Urban, Building,
Neighborhood, Road,
Housing, City, Town,
Intersection
Rural Town of Cazenovia, NY
ML assigns labels:
Landscape, Outdoors, Nature,
Scenery, Aerial View, Land, Urban,
Road, Housing, Building, Yard,
Neighborhood
KNOWN
TEST
AREAS

9. FINDINGS FROM KNOWN TEST AREAS
• For the City Center in New York City, NY – ML assigns labels “Urban” with
a 94% confidence. For the rural Town of Cazenovia, NY – ML assigns labels
“Urban” with a 76% confidence: A typical gap of about 15% points between
True Positive (TP) and False Positive (FP).
• Hybrid, Roadmap and Terrain images add noise.
• Real world applicability – If it reinforces what people on the ground
already know, it would be really helpful to Global MapAid donors and
volunteers. Applied ML - Harsh Prakash 9
Urban
Rural
City of Addis Ababa

10. TODO
• Use other datasets to augment data for BI applications. E.g. Census, IRS,
web searches, survey data from USAID and World Bank, etc.
• Use K-Nearest neighbors algorithm (k-NN) for pattern recognition to
predict for blind spots, and transform ML labels to vector.
• Use Cloud-based ML to identify patterns early and predict natural
disasters using weather data, food data and agricultural data.
• If ground volunteers or local mining companies confirm charcoal fires
and/or cooking burners on satellite images, then tune model further.Applied ML - Harsh Prakash 10
Regression for website visitor profile
using Census data
Automatic clustering of popular searches
on medlineplus.gov for May, 2015, using R
STAT, PostGIS

11. Applied ML - Harsh Prakash 11
POTENTIAL AT NASA
• ML and geoanalytics to explore LANDSAT data, and satellite and HELIOS
images –
• Modeling, Analysis and Prediction (MAP) Program – Black Marble maps of
night lights to gain insight on human activity.
• Auto-tagging of media – image, audio and video. E.g. Training videos.
• Log and text analyses.
• Smarter storage. E.g. S3 Intelligent Tiering.
• Solar storms.

12. Applied ML - Harsh Prakash 12
POTENTIAL AT NASA
Solar Storm
ML assigns labels:
Nature, Flare, Light,
Outdoors, Sun, Sky,
Night, Astronomy,
Universe, Outer Space,
Space, Moon, Sunrise,
Mountain, Planet
Solar Storm
ML assigns labels:
Night, Nature, Space,
Outdoors, Universe, Moon,
Astronomy, Outer Space,
Sun, Sky, Flare, Light,
Mountain, Photo,
Photography

13. NEXT STEPS
• Model as a Service – ML Models on AWS Marketplace.
• Frameworks and Tools – Rekognition, Google Vision, Microsoft Computer
Vision, TensorFlow, PyTorch, Jupyter Notebook, AWS SageMaker, R STAT.
• Questions?
Applied ML - Harsh Prakash 13
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