{"id":14568,"date":"2020-12-01T10:38:05","date_gmt":"2020-12-01T09:38:05","guid":{"rendered":"https:\/\/www.univ-smb.fr\/listic\/?p=14568"},"modified":"2025-05-13T15:35:39","modified_gmt":"2025-05-13T13:35:39","slug":"learning-fusion-and-remote-sensing-afute","status":"publish","type":"post","link":"https:\/\/www.univ-smb.fr\/listic\/pages-en\/learning-fusion-and-remote-sensing-afute\/","title":{"rendered":"Learning, Fusion and Remote Sensing (AFuT\u00e9)"},"content":{"rendered":"

French version\u00a0<\/a>\"\"<\/a><\/p>\n

Group facilitator<\/h3>\n
\n
\n
\n
Ammar Mian<\/a><\/div>\n

Issue and perspectives<\/h3>\n

Artificial intelligence<\/strong>\u00a0is at the heart of technological, scientific, economic, societal and environmental transformations. In this context, data modeling for description, decision, prediction and\/or forecasting purposes becomes an essential issue. Our workgroup is dedicated to this issue and brings together methodological works dealing with\u00a0machine learning<\/strong>\u00a0(deep learning, data mining),\u00a0the fusion of uncertain data<\/strong>\u00a0(probabilities, possibilities, belief functions, fuzzy sets, intervals) and\u00a0signal processing<\/strong>\u00a0(wavelets, statistical learning, differential geometry). These works are mainly applied to\/triggered by the analysis of\u00a0remote sensing<\/strong>\u00a0data with a\u00a0temporal dimension<\/strong>; most often for\u00a0environmental monitoring<\/strong>\u00a0purposes (crustal deformation, erosion, deforestation, glacial retreat, marine pollution). Remote sensing work is also being carried out to produce such data, with the main objectives of measuring displacements, detecting changes and inverting models. Our scientific perspectives are multiple and concern 1) the consideration of the volume, uncertainty and complexity (spatial, temporal, physical properties) of data, 2) the fusion of data and\/or models, and 3) the interpretability of the obtained results.<\/p>\n

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Keywords<\/strong><\/h3>\n
Deep learning, data mining, data fusion, uncertainties, remote sensing, time series, environmental monitoring.<\/div>\n<\/div>\n<\/div>\n
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Internship and employment opportunities at LISTIC<\/a><\/h4>\n<\/div>\n<\/div>\n

News<\/h3>\n

Soutenance de th\u00e8se de Olivier Lerda, le 17 d\u00e9cembre 2024 : D\u00e9tection multivari\u00e9e robuste pour sonar \u00e0 croix de Mills<\/a><\/p>\n

Soutenance de th\u00e8se de Hugo Brehier, le 4 d\u00e9cembre 2024: D\u00e9tection pour l\u2019Imagerie Radar \u00e0 Travers Murs par d\u00e9compositions de rang faible et parcimonieuse<\/a><\/p>\n

Soutenance de th\u00e8se de Micha\u00ebl Dell\u2019aiera le 12 octobre 2024:Des simulations aux donn\u00e9es r\u00e9elles, sur la pertinence des mod\u00e8les d\u2019apprentissage profond et de l\u2019adaptation de domaine : Application \u00e0 l\u2019astrophysique avec CTAO et LST-1<\/a><\/p>\n

Atelier SAR & Cryosphere, Annecy, 19 Septembre 2024<\/span><\/a><\/p>\n

Soutenance de th\u00e8se de Suvrat Kaushik,le 23 Janvier 2023: Ice aprons and hanging glaciers: new insights from optical and SAR remote sensing of the MontBlanc massif (western European Alps)<\/a><\/p>\n

Soutenance de th\u00e8se : E. Amri, Automatic Offshore Oil Detection Based On Deep Learning Approaches Using Heterogeneous Data Fusion, 27 juin 2022.<\/a><\/p>\n

Prix du meilleur article \u00e9tudiant 2020 d\u00e9cern\u00e9 \u00e0 Guilhem Marsy par La Revue Fran\u00e7aise de Photogramm\u00e9trie et de T\u00e9l\u00e9d\u00e9tection<\/a> pour sa publication \u00ab D\u00e9tection automatique de zones en mouvement dans des s\u00e9ries d\u2019images non recal\u00e9es : Application \u00e0 la surveillance des mouvements gravitaires \u00bb parue dans le num\u00e9ro 217-218 sp\u00e9cial du Colloque CFPT 2018<\/a>.<\/p>\n

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<\/div>\n<\/div>\n
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Publications<\/h3>\n

Referenced in HAL.<\/a><\/p>\n<\/div>\n

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Ongoing projects<\/h3>\n

Project ATROCE : Study of Frugal Federated Learning<\/p>\n

ANR REPED-SARIX: Estimation and recursive prediction of Earth deformation from SAR image time series. 2022-2024.<\/p>\n

CNES\/TOCSA project SITS Deep : Monitoring of natural territories by satellite optical imaging and deep learning. In collaboration with UMR TETIS and UMR IMS. 2022.<\/p>\n

Project iXblue: use of robust statistical methods for object detection and classification in sonar data. iXblue\/LISTIC PhD subject. 2020-2024.<\/p>\n

CNES\/PNTS project SHARE: chronological series of sentinel-1 SAR images in mountainous terrains. Change detection over snow-covered surfaces (dry snow\/moist snow) using automatic learning methods. In collaboration with UMR ISTerre, UMR LJK, CNRM, Magellium. 2021-2023.<\/p>\n

Members<\/h3>\n

Professors and associate professors:<\/strong> A. Atto<\/a>, A. Benoit<\/a>, Ph. Bolon<\/a>, R. Boukezzoula<\/a>, D. Coquin<\/a>, Y. Dumond<\/a>, M-P. Huget<\/a>, G. Mauris<\/a>, A. Mian<\/a>, E. Trouv\u00e9<\/a>, Y. Yan<\/a>, G. Ginolhac<\/a>, N. M\u00e9ger<\/a>, A. Bhanot<\/a>, Y. Mhiri<\/a>, C. Lin-Kwong-Chong<\/a>, A. Lavault<\/a>.<\/span><\/p>\n

Ph.D Students :<\/strong> D. El Hajjar<\/a>, D.Jafuno<\/a>, E. Moliere<\/a>, F.-X. Sikoumo Hogue<\/a>, M. Verlynde<\/a>, L. Zuccali,<\/a> S. Bouaziz<\/a>, M. Salis<\/a><\/span><\/p>\n

Completed projects<\/h3>\n

Project AFREU : Study of Frugal Federated Learning<\/p>\n

Project SMGA: use of AI techniques to detect cavities on mountain slopes. In collaboration with G\u00e9olithe. 2021.<\/p>\n

Project RINA: creation of a demonstrator using AI methods for an operational management of geological Natural Hazards. In collaboration with CEREMA, BRGM and G\u00e9olithe. 2021.<\/p>\n

Project Heliocity: classification of solar installation monitoring data using machine learning methods. 2021.<\/p>\n

ANR MARGARITA:<\/a>\u00a0Modern Adaptive Radar: Great Advances in Robust and Inference Techniques and Application. 2019-2021.<\/p>\n

Project TOTAL: oil slick detection, large ocean surface SAR data volumes, heterogeneous data fusion, deep learning. 2018-2021.<\/p>\n

ANR ReVeRIES:<\/a>\u00a0recreational, interactive and educational plant recognition on smartphones. 2016-2021.<\/p>\n

Project LDI I-TURN: real-time monitoring and control of a bar-turning machine using fuzzy systems (fuzzy rules, rule aggregation). 2020.<\/p>\n

Project SmarterPlan\/Linksium: detection and inventory of business objects by deep learning in 360\u00b0 images of commercial building interiors. Funded by SATT Linksium. 2020.<\/p>\n

CNES\/TOSCA project START Deep: monitoring of vegetated territories using remote sensing and deep learning techniques. In collaboration with UMR TETIS and UMR IMS. 2020.<\/p>\n

Project G\u00e9olithe : AI methods for processing data produced by airborne geological radars. 2020.<\/p>\n

CNES\/PNTS project: missing data restoration in displacement time series obtained from SAR images by statistical learning. 2019-2020.<\/p>\n

GammaLearn: characterization of gamma radiationS by deep learning approaches applied to Cherenkov images provided by a single telescope. In cooperation with UMR LAPP, jointly funded by the European project ASTERICS and the Savoie Mont Blanc Foundation. 2017-2020.<\/p>\n

ANR PHOENIX:<\/a>\u00a0parsimony, Huge Observations of Earth Non-stationarities from Images Time Series. 2015-2019.<\/p>\n

ANR VIP-Mont Blanc:<\/a>\u00a0understanding and predicting environmental changes: a research project on the morphological evolution of the Mont Blanc massif. 2014-2018.<\/p>\n

FUI G4M:<\/a>\u00a0multi-profession and multi-material geodetection. 2014-2017.<\/p>\n

FUI MISAC:<\/a>\u00a0multi-functional\u00a0Intelligent Surface\u00a0for Automative and\u00a0Aeronautics\u00a0Cockpits. 2012-2015.<\/p>\n

European project INTERREG GLARISKALP :<\/a>\u00a0risque glaciaire. 2011-2013.<\/p>\n

ANR FOSTER:<\/a>\u00a0spatiotemporal data mining: application to the understanding and monitoring of erosion. 2011-2013.<\/p>\n

ANR REVES:<\/a>\u00a0plant recognition for smartphone interfaces. 2010-2013.<\/p>\n

ANR EFIDIR:<\/a>\u00a0information extraction and fusion for the measurement of displacements using radar imaging. 2008-2012.<\/p>\n

Project ADIXEN: event forecasting in datastreams for predictive maintenance. 2007-2010.<\/p>\n<\/div>\n

ACI MEGATOR:<\/a>\u00a0measurement of the evolution of alpine glaciers by optical and radar remote sensing. 2004-2007.<\/p>\n<\/div>\n<\/article>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

French version\u00a0 Group facilitator Ammar Mian Issue and perspectives Artificial intelligence\u00a0is at the heart of technological, scientific, economic, societal and environmental transformations. In this context, data modeling for description, decision, prediction and\/or forecasting purposes becomes […]<\/p>\n","protected":false},"author":222,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","_seopress_robots_follow":"","_seopress_robots_imageindex":"","_seopress_robots_snippet":"","_seopress_robots_primary_cat":"none","_seopress_robots_breadcrumbs":"","_seopress_robots_freeze_modified_date":"","_seopress_robots_custom_modified_date":"","_seopress_robots_canonical":"","_seopress_social_fb_title":"","_seopress_social_fb_desc":"","_seopress_social_fb_img":"","_seopress_social_fb_img_attachment_id":0,"_seopress_social_fb_img_width":0,"_seopress_social_fb_img_height":0,"_seopress_social_twitter_title":"","_seopress_social_twitter_desc":"","_seopress_social_twitter_img":"","_seopress_social_twitter_img_attachment_id":0,"_seopress_social_twitter_img_width":0,"_seopress_social_twitter_img_height":0,"_seopress_redirections_value":"","_seopress_redirections_enabled":"","_seopress_redirections_enabled_regex":"","_seopress_redirections_logged_status":"both","_seopress_redirections_param":"","_seopress_redirections_type":301,"_seopress_analysis_target_kw":"","_seopress_news_disabled":"","_seopress_video_disabled":"","_seopress_video":[],"_seopress_pro_schemas_manual":[],"_seopress_pro_rich_snippets_disable_all":"","_seopress_pro_rich_snippets_disable":[],"_seopress_pro_schemas":[],"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[58],"tags":[],"class_list":["post-14568","post","type-post","status-publish","format-standard","hentry","category-pages-en"],"acf":[],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/posts\/14568","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/users\/222"}],"replies":[{"embeddable":true,"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/comments?post=14568"}],"version-history":[{"count":7,"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/posts\/14568\/revisions"}],"predecessor-version":[{"id":21620,"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/posts\/14568\/revisions\/21620"}],"wp:attachment":[{"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/media?parent=14568"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/categories?post=14568"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.univ-smb.fr\/listic\/wp-json\/wp\/v2\/tags?post=14568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}