Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya - PowerPoint PPT Presentation

Nov 05, 2022 •133 likes •291 views

Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya AI for Good Research Lab Background and Motivation Glaciers in the Hindu Kush Himalaya (HKH) are ecologically and societally important, and are at risk due to climate change

Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya AI for Good Research Lab
Background and Motivation • Glaciers in the Hindu Kush Himalaya (HKH) are ecologically and societally important, and are at risk due to climate change • Monitoring changes is key for water resource and glacial hazard management • The International Centre for Integrated Mountain Development (ICIMOD) curates a Regional Database System to support glacier monitoring of the HKH 2
Current Workflow • Derive spatial data by semi-automatically annotating landsat images - Data are used by scientific and policy communities • Labels are available dating back to 1990 and across the HKH An example of hyperpixel editing in the current labeling workflow. 3
Problem Description • Delineating glaciers is time consuming and challenging to scale • Manual interventions are needed to account for cloud cover, variable snow conditions, and supra-glacial debris Goal: • To effectively demarcate the boundaries of glaciers at different time points • Utilize machine learning to accelerate the mapping workflow 4
Problem Formulation Given a training dataset, semantic segmentation methods learn to assign pixel-level labels Y over input images X X: Cropped Landsat image • 10 channels from Landsat 7 • Add NDVI, NDSI, NDWI • Add SRTM Elevation / Slope Y: Pixel-level glacier labels • Background • Clean ice glacier Debris-covered glacier • Y: Glacier labels X: Input patch 5
Our Approach • Prepare data for modeling • Train semantic segmentation • Use web tool to incorporate feedback 6 6
Challenges and Takeaways: Band Selection • Best performance per subset of bands. • Importance of domain knowledge vs. automatic selection. 7
Challenges and Takeaways: Error Analysis and Debris Discovery • We have compared segmenting glaciers vs. differentiating segmentation of clear ice glaciers and debris-covered ones. • Same overall performance with small amount of debris-covered data. • The gap increases when we have more debris data. (Up to 16% difference when evaluating for area of more than 10% coverage of debris) 8
Challenges and Takeaways: Generalization to New Areas • How is the model is going to work in other geographic areas? • No performance difference is shown when restricting testing geographically. • There study area might be homogenous. 9
10
Glacier Mapping Web Tool
Code & Dataset Code: https://github.com/krisrs1128/glacier_mapping Data: http://lila.science/datasets/hkh-glacier-mapping Microsoft Confidential 12
Next Steps • Formal comparison with semi-automated approach • Use proposed approach for a glacier change analysis 1990 2000 2020 13
Thank you. 14

Recommend

The Hindu Kush Himalaya Assessment Mountains, Climate Change, Sustainability and People Dr.

The Hindu Kush Himalaya Assessment Mountains, Climate Change, Sustainability and People Dr. Ghulam Rasul Regional Program Manager ICIMOD The he Hin indu du Kush sh Him imalaya alaya Global asset for food, energy, water, carbon, and

570 views • 13 slides

Hindu & Hindutva Hindu & Hindutva : Name & Nationalism

Hindu & Hindutva Hindu & Hindutva : Name & Nationalism _______________________________ Swami Vigyananand HINDU : THE NAME THE PART 1 Contents: Origin of Hindu Word Be Proud as Hindu Core of Hindu Nation

2.57k views • 173 slides

experiences from the World Glacier Monitoring Service on data compilation, dissemination &

Reproducible Research Workshop, GIUZ, 14 SEP 2016 Reproducible Research: experiences from the World Glacier Monitoring Service on data compilation, dissemination & versioning Michael Zemp World Glacier Monitoring Service, Dept. Geography,

300 views • 11 slides

Hindu Clinical Chaplaincy Initiative HMEC & HAF Hindu Mandir Executives' Conference 1

Hindu Clinical Chaplaincy Initiative HMEC & HAF Hindu Mandir Executives' Conference 1 August 17-18, 2012, San Jose, CA Background and the Basics Pathway to a Hindu American Chaplaincy: An HAF- HMEC Joint Initiative Why do Hindu

293 views • 7 slides

Why Im NOT Why Im NOT Why Im NOT Why Im NOT a Hindu Why Im NOT a Hindu

Why Im NOT Why Im NOT Why Im NOT Why Im NOT a Hindu Why Im NOT a Hindu Why Im NOT Sanskrit for the mantra OM Why Im NOT Why Im NOT a Hindu Why Im NOT a Hindu Why Im NOT Sanskrit for

1.26k views • 105 slides

An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning http://www.cs.iastate.edu/~cs573x/bbsilab.html Machine Learning Software Preparing Data Building Classifiers Interpreting Results Machine Learning Software

496 views • 26 slides

Presentation: Snow and Glacier Monitoring Conference Paper October 2009 CITATIONS READS 0

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/281626649 Presentation: Snow and Glacier Monitoring Conference Paper October 2009 CITATIONS READS 0 122 1 author: A. V. Kulkarni

670 views • 27 slides

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by which computers can be trained through observation, rather than being explicitly programmed. Basically, a program is given input and adjusts its

556 views • 17 slides

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

872 views • 29 slides

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

398 views • 26 slides

Instrumentation, Observability, and Monitoring of Machine Learning Models 1 About Me Google

Instrumentation, Observability, and Monitoring of Machine Learning Models 1 About Me Google Engineer (2007-11) Clouderas Director of Data Science (2011-15) Slacks Director of Data Engineering (2015-2017) Slack Engineer

935 views • 30 slides

CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

1/22/19 CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine Learning? A Simple Task: Recognize Obama How do you program a computer to Recognize faces? Recommend movies? Decide which web

581 views • 5 slides

Xanterra Glacier National Park Lodges Glacier National Park, Montana

Xanterra Glacier National Park Lodges Glacier National Park, Montana glaciernationalparklodges.com @xanterratravel About Xanterra Glacier National Park Lodges: Known for its seemingly endless acres of rugged and breathtaking

334 views • 19 slides

Machine Learning for monitoring the condition of critical systems Ross W Dickie MacTaggart Scott

Machine Learning for monitoring the condition of critical systems Ross W Dickie MacTaggart Scott | Heriot Watt University rwd2@hw.ac.uk, Ross.Dickie@mactag.com #UDT2019 Stand: A19 Project Outline Industry & Academic partnership

476 views • 24 slides

Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their performance We use machine learning in situations where it is very challenging (or impossible) to define the rules by hand: e.g. face detection

1.04k views • 28 slides

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine

Introduction to Machine Learning COMPSCI 371D Machine Learning COMPSCI 371D Machine Learning Introduction to Machine Learning 1 / 12 Outline 1 Nearest Neighbor Prediction 2 Complexity Considerations 3 The Voronoi Diagram 4 Overfitting

564 views • 12 slides

1 Why Study Machine Learning? Why Study Machine Learning? Cognitive Science The Time is Ripe

What is Learning? Herbert Simon: Learning is any process by which a system improves performance from CS 391L: Machine Learning experience. Introduction What is the task? Classification Problem solving / planning /

603 views • 6 slides

MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING

JACOPO DI IORIO, MATTEO FONTANA, DANIELE OCCHIUTO, CLAUDIA VOLPETTI MACHINE LEARNING, STATISTICAL LEARNING AND PARALLEL COMPUTING INTRODUCTION VS MACHINE LEARNING STATISTICAL LEARNING Separate evolutions, but with shared methodologies MERGE

565 views • 55 slides

Apache PredictionIO End-to-End Machine Learning Server with Apache Spark What is Machine

Apache PredictionIO End-to-End Machine Learning Server with Apache Spark What is Machine Learning? What is Machine Learning? Examples of machine learning applications: Facial recognition software E-mail spam detectors Automated

302 views • 17 slides

Machine Learning 11 AI Slides (6e) c Lin Zuoquan@PKU 1998-2020 11 1 11 Machine Learning

Machine Learning 11 AI Slides (6e) c Lin Zuoquan@PKU 1998-2020 11 1 11 Machine Learning 11.1 Learning agents 11.2 Inductive learning 11.3 Deep learning 11.4 Statistical learning 11.5 Reinforcement learning 11.6 Transfer learning

1.7k views • 159 slides

Deep Learning: Intro Juhan Nam Review of Traditional Machine Learning The traditional machine

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Deep Learning: Intro Juhan Nam Review of Traditional Machine Learning The traditional machine learning pipeline Temporal Frame-level Unsupervised Classifier Summary

613 views • 27 slides

Machine Learning for Auto Optimization What is Machine Learning? Definition: Machine

Machine Learning for Auto Optimization What is Machine Learning? Definition: Machine learning refers to any system where the performance of a machine in performing a task improves by gaining more experience in performing that task .

394 views • 27 slides

MACHINE LEARNING kernels 1 MACHINE LEARNING 2012 MACHINE LEARNING Kernels: Intuition How

MACHINE LEARNING 2012 MACHINE LEARNING MACHINE LEARNING kernels 1 MACHINE LEARNING 2012 MACHINE LEARNING Kernels: Intuition How to separate the red class from the grey class? x 2 360 r x 1 Polar coordinates Data

1.04k views • 44 slides

BBM406 Fundamentals of Machine Learning Lecture 2: Machine Learning by Examples, Nearest

photo:@rewardyfahmi // Unsplash BBM406 Fundamentals of Machine Learning Lecture 2: Machine Learning by Examples, Nearest Neighbor Classifier Aykut Erdem // Hacettepe University // Fall 2019 When Do We Use Machine Learning? ML is

1.33k views • 110 slides