Source: 1ère Journée thématique sur le deep learning (DL)https://hal.inrae.fr/hal-048296261ère Journée thématique sur le deep learning (DL), Réseau 2Neurones, Oct 2024, Nantes, France

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Book

Le nexus Eau-Énergie-Alimentation dans les petites îles

Authors : Authier, Romain; Pillot, Benjamin; Guimbretiere, Guillaume ...

Subjects: Nexus Eau-Energie-Alimentation; Optimisation; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation

Source: SCIENCE de la DURABILITÉ ; https://hal.science/hal-04381836 ; SCIENCE de la DURABILITÉ, A paraître

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Academic Journal

Historical Documents and Automatic Text Recognition: Introduction

Authors : Pinche, Ariane; Stokes, Peter, Anthony;

Subjects: ATR; eScriptorium; Kraken

Source: EISSN: 2416-5999 ; Journal of Data Mining and Digital Humanities ; https://hal.science/hal-04508874 ; Journal of Data Mining and Digital Humanities, 2024, Historical

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Book

Ways of STICS use

Authors : Beaudoin, Nicolas; Constantin, Julie; Graux, Anne-Isabelle ...

Subjects: Long-term simulations; Crop model; Crop model STICS

Source: Stics Soil Crop Model. Conceptual framework, equations and uses ; https://hal.inrae.fr/hal-04457373 ; Nicolas Beaudoin; Patrice Lecharpentier; Dominique Ripoche-Wachter; Loïc Strullu; Bruno Mary;

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Conference

Spatio-temporal modelling of relationship between Organic Carbon Content and Land Use using Deep Learning approach and several covariables: application to the soils of the Beni Mellal in Morocco

Authors : Gadal, Sébastien; Oukhattar, Mounir; Keller, Catherine ...

Subjects: SOCS; LULC; Remote sensing

Source: GISTAM 2023 9th International Conference on Geographical Information Systems Theory, Applications and Management ; https://hal.science/hal-04080531 ; GISTAM 2023 9th

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Conference

Multi-temporal recognition of built-up area and land cover changes using machine learning approach in the Metropolis of Aix-Marseille-Provence in France

Authors : Gadal, Sébastien; Oukhattar, Mounir; Otobo, Solomon Abody ...

Subjects: Urbanisation; Land Cover Change Prediction; Remote sensing

Source: Joint Urban Remote Sensing Event (JURSE) 2023 ; https://hal.science/hal-04127519 ; Joint Urban Remote Sensing Event (JURSE) 2023, IEEE, pp.1-4, 2023,

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Conference

Urban Remote Sensing: Last Advances

Authors : Gadal, Sébastien;

Subjects: Urban remote sensing; Geo-simulation; UrbanismKaunas; Lithuania

Source: Smart Built Environment ; https://hal.science/hal-04322342 ; Smart Built Environment, Kaunas University of Technology; Faculty of Civil engineering and architecture, Nov

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Conference

Comprendre la composition des légendiers à l’aide des méthodes numériques ; Comprendre la composition des légendiers à l’aide des méthodes numériques: Acquisition automatique du texte et analyses stylométriques

Authors : Pinche, Ariane;

Subjects: Hagiographie -- Moyen âge -- France; Stylométrie; HTR for medieval French manuscriptsParis; France

Source: CBMAhag – Ateliers 2022 ; https://hal.science/hal-03930385 ; CBMAhag – Ateliers 2022, Elena Magnani, Jan 2023, Paris, France

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نتائج البحث

فلترة النتائج

Are LSTM and conceptual rainfall-runoff models able to cope with limited training datasets under diverse hydrometeorological conditions?

Identifying maize architectural ideotypes through 3D structural model validated in the field: Assessing the impact of plant architecture and sowing pattern to improve canopy light regime

Phénotypage haut-débit de la diversité génétique des arbres fruitiers pour une meilleure adaptation au changement climatique (PHADA) - Segmentation des parties boisées

Le nexus Eau-Énergie-Alimentation dans les petites îles

Historical Documents and Automatic Text Recognition: Introduction

Ways of STICS use

Spatio-temporal modelling of relationship between Organic Carbon Content and Land Use using Deep Learning approach and several covariables: application to the soils of the Beni Mellal in Morocco

Multi-temporal recognition of built-up area and land cover changes using machine learning approach in the Metropolis of Aix-Marseille-Provence in France

Urban Remote Sensing: Last Advances

Comprendre la composition des légendiers à l’aide des méthodes numériques ; Comprendre la composition des légendiers à l’aide des méthodes numériques: Acquisition automatique du texte et analyses stylométriques

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