This document discusses how AI and data science can help protect biodiversity by detecting, identifying, and counting animals using computer vision models. It provides examples of projects that use deep learning to identify salmon from underwater videos, classify species from photos shared on iNaturalist, estimate penguin counts from images, and detect elephants spotted via aerial surveys to help conservation efforts. The document emphasizes the importance of having adequate training data and combining automated methods with human inputs and validation to accurately protect threatened species.
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AI for Social Good - Saving the Planet with Data Science
1. AI for Social Good: Saving the planet with
Data Science
Ganes Kesari, Gramener
@kesaritweets
2. 2Gramener
Introduction
Co-founder & Head of
Analytics
100+ Clients
Insights as Stories
Help apply & adopt
Analytics
Our data science platform,
Gramex is now open-sourced!
“Simplify Data Science
for all”
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What ails our Biodiversity?
Now Extinct!
Movie copyright: Blue Sky Studios
Image by AngieToh from Pixabay
Now Extinct…
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Spotting, Identifying and Counting Animals to Save them
Live Case Studies from Gramener’s partnership with Microsoft AI for
Earth
https://www.microsoft.com/en-us/ai/ai-for-earth
Climate Agriculture Biodiversity Water
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Input Output
Identify features to
teach model
Traditional Machine Learning
Person
Name
9
Deep Learning
Input Output
Model automatically identifies
features to learn
Person
Name
https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
Deep Learning to the rescue
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Counting Crowds – Bounding boxes vs Density estimates
Occlusion
Density Difference
Perspective Distortion
Camera angle
Preserve spatial information
Localize count
Handle scale variations
No longer looking for a head!
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Approach
• High-level prior to classify image into buckets
• Density estimation to create the density map
• NC6 v3 virtual machine with V100 GPU card
• Trained for 200 epochs, MAE for the model : ~10.5
https://arxiv.org/pdf/1707.09605.pdf Work done by Gramener in partnership with Microsoft AI for Earth
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6 Deep Learning Takeaways: When the Rubber hits the Road
Scout for the
right Data
Watch out for
labelling
Educate on
Accuracy
Augment with
human inputs
Build into a
workflow
Budget refresh
& upkeep
37. Session deck with model details, Github repos & bonus case study at
@kesaritweetsgramener.com @kesari
gkesari.com/strata