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

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