{"id":726268,"date":"2021-02-19T12:21:03","date_gmt":"2021-02-19T20:21:03","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=726268"},"modified":"2022-11-17T10:11:39","modified_gmt":"2022-11-17T18:11:39","slug":"machine-learning-for-glacier-monitoring-in-the-hindu-kush-himalaya","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/machine-learning-for-glacier-monitoring-in-the-hindu-kush-himalaya\/","title":{"rendered":"Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya"},"content":{"rendered":"

Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and correct model predictions, with the ultimate aim of accelerating the glacier mapping process.<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,198583],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[246694,252520,246691,252529,252517,252532,246685,252526,252523,252535],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-726268","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-ecology-environment","msr-locale-en_us","msr-field-of-study-artificial-intelligence","msr-field-of-study-climate-change","msr-field-of-study-computer-science","msr-field-of-study-ecological-monitoring","msr-field-of-study-glacier","msr-field-of-study-hindu-kush","msr-field-of-study-machine-learning","msr-field-of-study-satellite","msr-field-of-study-satellite-imagery","msr-field-of-study-web-tool"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-12-8","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.climatechange.ai\/papers\/neurips2020\/57","label_id":"243109","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.climatechange.ai\/papers\/neurips2020\/57\/paper.pdf","label_id":"243132","label":0}],"msr_related_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/github.com\/krisrs1128\/glacier_mapping","label_id":"264520","label":0}],"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Shimaa Baraka","user_id":0,"rest_url":false},{"type":"text","value":"Benjamin Akera","user_id":0,"rest_url":false},{"type":"text","value":"Bibek Aryal","user_id":0,"rest_url":false},{"type":"text","value":"Tenzing Sherpa","user_id":0,"rest_url":false},{"type":"text","value":"Finu Shresta","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Anthony Ortiz","user_id":39715,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Anthony Ortiz"},{"type":"text","value":"Kris Sankaran","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Juan M. 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