In this article, we compare several approaches to segmenting glacial lakes in the Hindu Kush Himalayas in order to support glacial lake area mapping. More automatic mapping could support risk assessments of Glacial Lake Outburst Floods, a type of natural hazard that poses a risk to communities and infrastructure in valleys below glacial lakes. We propose and evaluate several approaches that incorporate labels from a 2015 survey using Landsat 7 ETM+ SLC-off imagery to guide segmentation on newer higher resolution satellite images such as Sentinel 2 and Bing Maps imagery, comparing them also to approaches that do not use this form of weak prior. We find that a guided version of U-Net and a properly initialized form of morphological snakes are most effective for these two datasets, respectively, each providing between an 8\u201310% intersection-over-union improvement over existing U-Net segmentation approaches. An error analysis highlights the strengths and limitations of each method. We design visualizations to support the discovery of lakes of potential concern, including an interactive exploratory interface. All the codes supporting our study are released in public repositories.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this article, we compare several approaches to segmenting glacial lakes in the Hindu Kush Himalayas in order to support glacial lake area mapping. More automatic mapping could support risk assessments of Glacial Lake Outburst Floods, a type of natural hazard that poses a risk to communities and infrastructure in valleys below glacial lakes. We […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2022-10-19","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,198583],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[259261],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-897240","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_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2022-10-19","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:\/\/ieeexplore.ieee.org\/abstract\/document\/9924600","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"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":"Weiyushi Tian","user_id":0,"rest_url":false},{"type":"guest","value":"tenzing-sherpa","user_id":820849,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=tenzing-sherpa"},{"type":"guest","value":"finu-shrestha","user_id":820858,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=finu-shrestha"},{"type":"guest","value":"mir-matin","user_id":820861,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mir-matin"},{"type":"user_nicename","value":"Rahul Dodhia","user_id":41401,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Rahul Dodhia"},{"type":"user_nicename","value":"Juan M. Lavista Ferres","user_id":39552,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Juan M. Lavista Ferres"},{"type":"guest","value":"kris-sankaran","user_id":819106,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=kris-sankaran"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[696544],"msr_project":[1014747,1016418,812350,815395],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":1014747,"post_title":"Fundamental Rights - AI for Good","post_name":"fundamental-rights-ai-for-good","post_type":"msr-project","post_date":"2024-04-02 08:58:55","post_modified":"2024-09-19 09:50:54","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/fundamental-rights-ai-for-good\/","post_excerpt":"Microsoft is committed to strengthening communities and empowering the organizations that help them thrive. We have a responsibility to protect people\u2019s fundamental rights, and help all communities succeed.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/1014747"}]}},{"ID":1016418,"post_title":"Advance Sustainability - AI for Good","post_name":"advance-sustainability-ai-for-good","post_type":"msr-project","post_date":"2024-04-02 08:57:43","post_modified":"2024-11-27 10:34:16","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/advance-sustainability-ai-for-good\/","post_excerpt":"Climate change requires swift, collective action and technological innovation. We are committed to meeting our own goals while enabling others to do the same.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/1016418"}]}},{"ID":812350,"post_title":"Geospatial Machine Learning","post_name":"geospatial-machine-learning","post_type":"msr-project","post_date":"2022-02-24 10:03:45","post_modified":"2024-04-19 14:52:45","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/geospatial-machine-learning\/","post_excerpt":"We combine geospatial data with machine learning in collaboration with partners at universities, conservation agencies, and NGOs in projects that support disaster response, humanitarian action and conservation efforts.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/812350"}]}},{"ID":815395,"post_title":"Glacier Mapping","post_name":"glacier-mapping","post_type":"msr-project","post_date":"2022-02-24 09:58:59","post_modified":"2022-02-24 09:59:01","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/glacier-mapping\/","post_excerpt":"Glacier mapping and glacial lakes monitoring uses a machine learning-based approach to support ecological monitoring.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/815395"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/897240","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/897240\/revisions"}],"predecessor-version":[{"id":897249,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/897240\/revisions\/897249"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=897240"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=897240"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=897240"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=897240"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=897240"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=897240"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=897240"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=897240"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=897240"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=897240"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=897240"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=897240"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=897240"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}