Search results for "Environment"

showing 10 items of 28487 documents

Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V

2018

Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…

010504 meteorology & atmospheric sciencesComputer sciencebusiness.industryMultispectral image0211 other engineering and technologiesPattern recognitionCloud computing02 engineering and technologySpectral bands01 natural sciencesConvolutional neural networkData modelingKey (cryptography)Artificial intelligencebusinessTransfer of learning021101 geological & geomatics engineering0105 earth and related environmental sciencesIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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FAME: Software for analysing rock microstructures

2016

Determination of rock microstructures leads to a better understanding of the formation and deformation of polycrystalline solids. Here, we present FAME (Fabric Analyser based Microstructure Evaluation), an easy-to-use MATLAB®-based software for processing datasets recorded by an automated fabric analyser microscope. FAME is provided as a MATLAB®-independent Windows® executable with an intuitive graphical user interface. Raw data from the fabric analyser microscope can be automatically loaded, filtered and cropped before analysis. Accurate and efficient rock microstructure analysis is based on an advanced user-controlled grain labelling algorithm. The preview and testing environments simplif…

010504 meteorology & atmospheric sciencesComputer sciencebusiness.industryOrientation (computer vision)AnalyserComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONcomputer.file_format010502 geochemistry & geophysics01 natural sciencesVisualizationSoftwareComputer graphics (images)Batch processingExecutableComputers in Earth SciencesbusinesscomputerSimulation0105 earth and related environmental sciencesInformation SystemsRock microstructureGraphical user interfaceComputers & Geosciences
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First Results of Hyperspectral Scene Generation in Preparation of the Chime Imaging Spectrometer Mission

2021

End-To-End mission performance simulators (E2Es) are software tools developed to support satellite mission preparatory activities. For passive remote sensing missions, E2Es generate synthetic scenes simulating the interaction of the solar radiation between the atmosphere and the surface; therefore allowing the estimation of the mission performance before its launch. In this paper, we present the CHIME Scene Generator Module (SGM) as part of CHIME E2Es, with state-of-the-art parallelization and optimization that give a performance allowing to obtain a whole year of daily worldwide Top-Of-Atmosphere radiance images in a matter of hours. The CHIME SGM generates 100x200km hyperspectral scenes w…

010504 meteorology & atmospheric sciencesComputer sciencebusiness.industryReal-time computing0211 other engineering and technologiesImaging spectrometerHyperspectral imaging02 engineering and technology01 natural sciencesConvolutionInstruction setSoftwareShadowRadianceSatellitebusiness021101 geological & geomatics engineering0105 earth and related environmental sciences2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
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Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

2020

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…

010504 meteorology & atmospheric sciencesComputer sciencehyperspectral image classificationScience0211 other engineering and technologiesgeoinformatics02 engineering and technologyneuroverkot01 natural sciencesConvolutional neural networkpuulajitPARAMETERSSet (abstract data type)LIDARFORESTSClassifier (linguistics)021101 geological & geomatics engineering0105 earth and related environmental sciencesbusiness.industryDeep learningspektrikuvausQHyperspectral imagingdeep learningPattern recognition15. Life on landmiehittämättömät ilma-aluksetPerceptron113 Computer and information sciencesClass (biology)drone imagery3d convolutional neural networksmetsänarviointiMACHINEkoneoppiminentree species classification3D convolutional neural networksGeneral Earth and Planetary SciencesRGB color modelArtificial intelligencekaukokartoitusbusinesshyperspectral image classificationRemote Sensing
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Estimating Missing Information by Cluster Analysis and Normalized Convolution

2018

International audience; Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.

010504 meteorology & atmospheric sciencesComputer sciencemedia_common.quotation_subjectReal-time computingEnergy Engineering and Power Technology02 engineering and technologyIterative reconstructionsmart city dealsCluster (spacecraft)01 natural sciencesIndustrial and Manufacturing Engineeringnormalized convolutionstandard clustering technique[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]ConvolutionArtificial IntelligenceSmart city11. Sustainability0202 electrical engineering electronic engineering information engineeringLimit (mathematics)SimplicityCluster analysisInstrumentationad-hoc sensors0105 earth and related environmental sciencesmedia_commonSettore INF/01 - InformaticaRenewable Energy Sustainability and the EnvironmentComputer Science Applications1707 Computer Vision and Pattern Recognitionenvironmental informationmissing informationComputer Networks and CommunicationKernel (image processing)020201 artificial intelligence & image processingcluster analysis2018 IEEE 4th International Forum on Research and Technology for Society and Industry (RTSI)
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Supporting group decision makers to locate temporary relief distribution centres after sudden-onset disasters

2020

International audience; In the humanitarian response, multiple decision-makers (DMs) need to collaborate in various problems, such as locating temporary relief distribution centres (RDCs). Several studies have argued that maximising demand coverage, reducing logistics costs and minimising response time are among the critical objectives when locating RDCs after a sudden-onset disaster. However, these objectives are often conflicting and the trade-offs can considerably complicate the situation for finding a consensus.To address the challenge and support the DMs, we suggest investigating the stability of non-dominated alternatives derived from a multi-objective model based on Monte Carlo Simul…

010504 meteorology & atmospheric sciencesComputer sciencemedicine.medical_treatment0211 other engineering and technologiesStability (learning theory)Distribution (economics)02 engineering and technology01 natural sciencesHumanitarian responseNATURAL DISASTERSupport groupINFORMATION-MANAGEMENT[SPI]Engineering Sciences [physics]NETWORK DESIGNGroup decision-making2015 Nepal earthquakemedicineOPTIMIZATIONVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550Monte Carlo simulation0105 earth and related environmental sciences021110 strategic defence & security studiesCOORDINATIONCOMPLEXDISTRIBUTION MODELbusiness.industrySTOCHASTIC-MODELHumanitarian responseGeologyGeotechnical Engineering and Engineering GeologyRisk analysis (engineering)Multiobjective facility locationPARETO SETbusinessSafety ResearchHUMANITARIAN LOGISTICSSudden onsetInternational Journal of Disaster Risk Reduction
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Stochastic Galerkin method for cloud simulation

2018

AbstractWe develop a stochastic Galerkin method for a coupled Navier-Stokes-cloud system that models dynamics of warm clouds. Our goal is to explicitly describe the evolution of uncertainties that arise due to unknown input data, such as model parameters and initial or boundary conditions. The developed stochastic Galerkin method combines the space-time approximation obtained by a suitable finite volume method with a spectral-type approximation based on the generalized polynomial chaos expansion in the stochastic space. The resulting numerical scheme yields a second-order accurate approximation in both space and time and exponential convergence in the stochastic space. Our numerical results…

010504 meteorology & atmospheric sciencesComputer scienceuncertainty quantificationQC1-999cloud dynamicsFOS: Physical sciencesCloud simulation65m15010103 numerical & computational mathematics01 natural sciencespattern formationMeteorology. ClimatologyFOS: MathematicsApplied mathematicsMathematics - Numerical Analysis0101 mathematicsStochastic galerkin0105 earth and related environmental sciencesnavier-stokes equationsPhysics65m2565l05Numerical Analysis (math.NA)65m06Computational Physics (physics.comp-ph)stochastic galerkin method35l4535l65finite volume schemesQC851-999Physics - Computational Physicsimex time discretization
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Effects of natural radiation damage on back-scattered electron images of single crystals of minerals

2006

Generally, it has been assumed that signal intensity variations in back-scattered electron (BSE) images of minerals are mainly controlled by chemical heterogeneity. This is especially true for images of single crystals, where effects of different crystal orientations with respect to the incident beam on the observed BSE are excluded. In contrast, we show that local variations of the structural state within single-crystals (i.e., degree of lattice order or lattice imperfectness) may also have dramatic effects on the back-scattering of electrons. As an example, we present BSE images of single-crystals of natural zircon, ZrSiO 4 , whose intensity patterns are predominantly controlled by struct…

010504 meteorology & atmospheric sciencesCondensed matter physicsChemistryMineralogyElectron010502 geochemistry & geophysics01 natural sciencesStructural heterogeneityCrystalGeophysicsGeochemistry and PetrologyRadiation damageIncident beamSignal intensity0105 earth and related environmental sciencesZirconChemical heterogeneityAmerican Mineralogist
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Soil organic carbon stock on the Majorca Island: temporal change in agricultural soil over the last 10 years

2019

8 Pags.- 5 Tabls.- 3 Figs.

010504 meteorology & atmospheric sciencesConservation agricultureLand managementMediterranean soil organic carbonspatial variationSpatial and temporal variationCarbon sequestration01 natural sciencestemporal changeSoil managementMediterranean soil carbonsoil carbon sequestrationAgricultural landGeostatistics0105 earth and related environmental sciencesEarth-Surface ProcessesAgroforestrySoil organic matter04 agricultural and veterinary sciencesSoil carbonCrop rotationMajorca carbon storage040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceCrop management
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Changements environnementaux survenant à la limite Oligocène/Miocène du bassin des Limagnes (Massif central, France).

2018

16 pages; International audience; Continental environments are very sensitive to climatic variations. A unique opportunity to study the climate changes around the Oligocene/Miocene boundary is offered by the Limagne graben Basin (France) where this stage boundary is well constrained by fossils. Indeed, some localities of the Limagne Graben Basin are so rich in mammal remains that they have become a European reference for mammal biostratigraphy. The dominant sedimentary facies of the lacustrine deposits in the northern part of the Limagne Graben Basin are composed of poorly cemented marls and calcarenites containing various plants and animals remains (e.g. algae, fish bones and teeth, gastro…

010504 meteorology & atmospheric sciencesContext (language use)Biostratigraphy010502 geochemistry & geophysics01 natural sciencesPaleontology[SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistrycarbonates lacustresMarl14. Life underwaterstromatolitebassin des Limagnes0105 earth and related environmental sciencesPalynologygeographygeography.geographical_feature_categoryLimagne Basinisotopes de l’oxygènebiologylimite oligo-miocèneoxygen isotopeslcsh:QE1-996.5GeologyMassif15. Life on landbiology.organism_classificationlacustrine carbonateGrabenlcsh:GeologyStromatolite13. Climate action[SDU.STU.ST]Sciences of the Universe [physics]/Earth Sciences/StratigraphyapatitePeriod (geology)Oligocene/Miocene boundarybiozoneGeologyBSGF - Earth Sciences Bulletin
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