Analytics with three-dimensional imagery from drones are driving the next generation of remote monitoring applications. Today, there is an unmet need in providing such analytics in an interactive manner, especially over weak Internet connections, to quickly diagnose and solve problems in the commercial industry space of monitoring assets using drones in remote parts of the world. Existing mechanisms either compromise on the quality of insights by not building 3D images and analyze individual 2D images in isolation, or spend tens of minutes building a 3D image before obtaining and uploading insights. We present Visage, a system that accelerates 3D image analytics by identifying smaller parts of the data that can actually benefit from 3D analytics and prioritizing building, and uploading the localized 3D images for those parts. To achieve this, Visage uses a graph to represent raw 2D images and their relative content overlap, and then identifies the various subgraphs using application knowledge that are good candidates for localized 3D image based insights. We evaluate Visage using data from multiple real deployments and show that it can reduce analytics-latency by up to four orders of magnitude.<\/p>\n","protected":false},"excerpt":{"rendered":"
Analytics with three-dimensional imagery from drones are driving the next generation of remote monitoring applications. Today, there is an unmet need in providing such analytics in an interactive manner, especially over weak Internet connections, to quickly diagnose and solve problems in the commercial industry space of monitoring assets using drones in remote parts of the […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[259330,247546,246685],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-779473","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us","msr-field-of-study-drone","msr-field-of-study-edge-computing","msr-field-of-study-machine-learning"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2021-10-1","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":"ACM","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":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/09\/Visage_Mobicom_2021.pdf","id":"779491","title":"visage_mobicom_2021","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":779491,"url":"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2021\/09\/Visage_Mobicom_2021.pdf"}],"msr-author-ordering":[{"type":"text","value":"Sagar Jha","user_id":0,"rest_url":false},{"type":"text","value":"Youjie Li","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Shadi Noghabi","user_id":37760,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Shadi Noghabi"},{"type":"user_nicename","value":"Vaishnavi Ranganathan","user_id":38085,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Vaishnavi Ranganathan"},{"type":"user_nicename","value":"Peeyush Kumar","user_id":39892,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Peeyush Kumar"},{"type":"text","value":"Andrew Nelson","user_id":0,"rest_url":false},{"type":"text","value":"Michael Toelle","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Sudipta Sinha","user_id":33748,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sudipta Sinha"},{"type":"user_nicename","value":"Ranveer Chandra","user_id":33344,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ranveer Chandra"},{"type":"user_nicename","value":"Anirudh Badam","user_id":31008,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Anirudh Badam"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[808747],"msr_group":[144899,714067],"msr_project":[881235],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":881235,"post_title":"Project FarmVibes","post_name":"project-farmvibes","post_type":"msr-project","post_date":"2022-10-06 08:00:00","post_modified":"2024-07-29 09:55:45","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-farmvibes\/","post_excerpt":"Democratizing digital tools for sustainable agriculture As one of the biggest contributors to climate change, agriculture, along with land use degradation and deforestation, account for about a quarter of the global GHG emissions and consumes about 70% of the world\u2019s freshwater resources. 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