LEADER 00797nam0-22002891i-450- 001 990006498160403321 005 20001010 035 $a000649816 035 $aFED01000649816 035 $a(Aleph)000649816FED01 035 $a000649816 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aPan-africanism in Action$eAn Account of the UAM$fAlbert Tevoedjre 210 $aHarvard$cCenter for International Affairs$d1965. 215 $a88 p.$d22 cm 676 $a320.5 700 1$aTévoédjrè,$bAlbert$0411651 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006498160403321 952 $aXIV E 1494$b14706$fFSPBC 959 $aFSPBC 996 $aPan-africanism in Action$9654190 997 $aUNINA DB $aGEN01 LEADER 02941 am 2200673 n 450 001 9910286406703321 005 20180612 010 $a979-1-02-401110-3 024 7 $a10.4000/books.purh.8244 035 $a(CKB)4100000006675334 035 $a(FrMaCLE)OB-purh-8244 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/44968 035 $a(PPN)23068405X 035 $a(EXLCZ)994100000006675334 100 $a20180926j|||||||| ||| 0 101 0 $afre 135 $auu||||||m|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aLe Développement du sport en Haute-Normandie $eBilan et perspective /$fJean-Pierre Lefèvre 210 $aMont-Saint-Aignan $cPresses universitaires de Rouen et du Havre$d2018 215 $a1 online resource (91 p.) 311 $a2-87775-001-9 330 $aL'organisation d'un colloque relatif au développement des Activités Sportives et de Loisir en Haute-Normandie, témoigne de l'intérêt que porte notre Université à la nécessaire revitalisation de notre région. Les Sciences et Techniques des Activités Physiques et Sportives en tant que discipline universitaire, ont pour vocation de nourrir une réflexion féconde sur les conditions nécessaires aux développements nouveaux que la Haute-Normandie peut espérer dans le cadre des mutations qu'elle subit. Développement du tourisme, des équipements de sport et de loisir, réaménagement des espaces ruraux, nouveaux styles de vie, nouveaux lieux de culture autant d'éléments de réflexion pour notre colloque. Les intervenants, nombreux et de qualité, nous offrent au travers de ces Annales une vue d'ensemble des résultats et des tendances actuelles. 517 $aDéveloppement du sport en Haute-Normandie 606 $aPolitical Science Public Admin. & Development 606 $aArea Studies 606 $aHospitality Leisure Sport & Tourism 606 $atourisme 606 $asport 606 $aloisir 606 $aéquipement 606 $aactivité physique 610 $atourisme 610 $aactivité physique 610 $aéquipement 610 $asport 610 $aloisir 615 4$aPolitical Science Public Admin. & Development 615 4$aArea Studies 615 4$aHospitality Leisure Sport & Tourism 615 4$atourisme 615 4$asport 615 4$aloisir 615 4$aéquipement 615 4$aactivité physique 700 $aBonnenfant$01281689 701 $aLefebvre$b Bernard$01281216 701 $aMichel$0717186 701 $aPociello$b Christian$01281690 701 $aSimon$b M$0349230 701 $aVidal$b H$01281691 701 $aLefèvre$b Jean-Pierre$01281692 801 0$bFR-FrMaCLE 906 $aBOOK 912 $a9910286406703321 996 $aLe Développement du sport en Haute-Normandie$93018576 997 $aUNINA LEADER 05921nam 2201501z- 450 001 9910557747903321 005 20220111 035 $a(CKB)5400000000045863 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76425 035 $a(oapen)doab76425 035 $a(EXLCZ)995400000000045863 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvanced Deep Learning Strategies for the Analysis of Remote Sensing Images 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (438 p.) 311 08$a3-0365-0986-0 311 08$a3-0365-0987-9 330 $aThe rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer-at least partially-such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching. 606 $aResearch and information: general$2bicssc 610 $a3D information 610 $aadversarial learning 610 $aanomaly detection 610 $aBatch Normalization 610 $abuilding damage assessment 610 $aCNN 610 $aconditional random field (CRF) 610 $aconvolution 610 $aconvolutional neural network 610 $aconvolutional neural networks 610 $aCycleGAN 610 $adata augmentation 610 $adeep convolutional networks 610 $adeep features 610 $adeep learning 610 $adensenet 610 $aDenseUNet 610 $adepthwise atrous convolution 610 $adesert 610 $adespeckling 610 $aedge enhancement 610 $aEfficientNets 610 $afaster region-based convolutional neural network (FRCNN) 610 $afeature engineering 610 $afeature fusion 610 $aframework 610 $agenerative adversarial networks 610 $aGenerative Adversarial Networks 610 $aglobal convolution network 610 $ahand-crafted features 610 $ahigh spatial resolution remote sensing 610 $ahigh-resolution remote sensing image 610 $ahigh-resolution remote sensing imagery 610 $ahigh-resolution representations 610 $ahyperspectral image classification 610 $aimage classification 610 $ainfrastructure 610 $aISPRS vaihingen 610 $aLandsat-8 610 $alifting scheme 610 $aLSTM 610 $aLSTM network 610 $amachine learning 610 $amapping 610 $amin-max entropy 610 $amisalignments 610 $amonitoring 610 $amulti-scale 610 $anearest feature selector 610 $aneural networks 610 $aobject detection 610 $aobject-based 610 $aOpen Street Map 610 $aopen-set domain adaptation 610 $aorthophoto 610 $aorthophotos registration 610 $aorthophotos segmentation 610 $aOUDN algorithm 610 $aoutline extraction 610 $apareto ranking 610 $apavement markings 610 $apixel-wise classification 610 $aplant disease detection 610 $apost-disaster 610 $aprecision agriculture 610 $aremote sensing 610 $aremote sensing imagery 610 $aresult correction 610 $aroad 610 $aroad extraction 610 $aSAR 610 $asatellite 610 $asatellites 610 $ascene classification 610 $asemantic segmentation 610 $aSentinel-1 610 $asingle-shot 610 $asingle-shot multibox detector (SSD) 610 $aSinkhorn loss 610 $asub-pixel 610 $asuper-resolution 610 $asynthetic aperture radar 610 $atext image matching 610 $atriplet networks 610 $atwo stream residual network 610 $aU-Net 610 $aUAV multispectral images 610 $aUnmanned Aerial Vehicles (UAV) 610 $aunsupervised segmentation 610 $aurban forests 610 $avisibility 610 $awater identification 610 $awater index 610 $awildfire detection 610 $axBD 615 7$aResearch and information: general 700 $aBazi$b Yakoub$4edt$01327926 702 $aPasolli$b Edoardo$4edt 702 $aBazi$b Yakoub$4oth 702 $aPasolli$b Edoardo$4oth 906 $aBOOK 912 $a9910557747903321 996 $aAdvanced Deep Learning Strategies for the Analysis of Remote Sensing Images$93038285 997 $aUNINA