Search results for "hybrid models"

showing 3 items of 3 documents

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

2021

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time seri…

2. Zero hungerland surface phenology (LSP)010504 meteorology & atmospheric sciencesScienceQGoogle Earth Engine (GEE)0211 other engineering and technologiesGaussian Process Regression (GPR)02 engineering and technology15. Life on land01 natural sciencescrop traitsGeneral Earth and Planetary Sciencesland surface phenology (LSP); Google Earth Engine (GEE); Gaussian Process Regression (GPR); Sentinel-2; gap-filling; crop traits; hybrid modelsSentinel-2gap-filling021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote Sensing
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Toward a Collective Agenda on AI for Earth Science Data Analysis

2021

In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to advance the modeling and understanding of the Earth system. Despite such great opportunities, we also observed a worrying tendency to remain in disciplinary comfort zones applying recent advances from artificial intelligence on well resolved remote sensing problems. Here we take a position on research directions where we think the interface between these fields will have the most impact and be…

Signal Processing (eess.SP)FOS: Computer and information sciences010504 meteorology & atmospheric sciencesGeneral Computer Science530 PhysicsInterface (Java)Computer Vision and Pattern Recognition (cs.CV)Earth sciencedata analysisComputer Science - Computer Vision and Pattern Recognition0211 other engineering and technologiesearth observation02 engineering and technology01 natural sciencesEnvironmental scienceData modelingFOS: Electrical engineering electronic engineering information engineeringClimate science1700 General Computer ScienceElectrical Engineering and Systems Science - Signal ProcessingElectrical and Electronic EngineeringInstrumentation021101 geological & geomatics engineering0105 earth and related environmental sciences11476 Digital Society Initiative3105 Instrumentation2208 Electrical and Electronic Engineering1900 General Earth and Planetary SciencesDeep learninginterpretable AIRemote sensingartificial intelligencehybrid modelsEarth system scienceAIRemote sensing (archaeology)10231 Institute for Computational ScienceGeneral Earth and Planetary SciencesPotential gameDisciplineIEEE Geoscience and Remote Sensing Magazine
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The performance investigation of viscoelastic hybrid models in vehicle crash event representation

2011

Aurthor's version of a chapter published in the book: Proceedings of the 18th IFAC World Congress 2011. Also available from the publisher at: http://dx.doi.org/10.3182/20110828-6-IT-1002.00284 This paper presents application of physical models composed of springs, dampers and masses joined together in various arrangements to simulation of a real car collision with a rigid pole. Equations of motion of those systems are being established and subsequently solutions to obtained differential equations are formulated. We start with a general model consisting of two masses, two springs, and two dampers, and illustrate its application to represent fore-frame and aft-frame of a vehicle. Hybrid model…

VDP::Mathematics and natural science: 400::Mathematics: 410::Applied mathematics: 413VDP::Technology: 500::Mechanical engineering: 570vehicle crash energy absorbers hybrid models kinematics modeling
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