LEADER 03251nam 2200541 450 001 9910788480503321 005 20230801232422.0 010 $a1-4411-5190-7 035 $a(CKB)3230000000213952 035 $a(StDuBDS)AH25461336 035 $a(MiAaPQ)EBC5309437 035 $a(Au-PeEL)EBL5309437 035 $a(CaPaEBR)ebr11518409 035 $a(OCoLC)1027206247 035 $a(MiAaPQ)EBC4948608 035 $a(Au-PeEL)EBL4948608 035 $a(CaONFJC)MIL851504 035 $a(OCoLC)1024261258 035 $a(EXLCZ)993230000000213952 100 $a20180314h20122012 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aIn search of the city on a hill $ethe making and unmaking of an American myth /$fRichard M. Gamble 205 $a1st ed. 210 1$aLondon, [England] ;$aNew York, New York :$cContinuum,$d2012. 210 4$dİ2012 215 $a1 online resource (208 p.) 300 $aIncludes index. 311 $a1-4411-6232-1 320 $aIncludes bibliographical references and index. 327 $aIntroduction \ 1. A Foreign Country \ 2. The Good Land \ 3. A Land of Light, 1630-1838 \ 4. A Spectacle to the World, 1838-1930 \ 5. TheaRevolutionary City, 1930-1969a\ 6. The Shining City, 1969-1989 \a7. The Once and Future Citya\ A Note on Sources \ Index 330 $a'In Search of the City on a Hill' challenges the widespread assumption that Americans have always used this potent metaphor to define their national identity. It demonstrates that America's 'redeemer myth' owes more to 19th and 20th century reinventions of the Puritans than to the colonists' own conceptions of divine election. 330 $bIn Search ofathe City on a Hillchallenges the widespread assumption that Americans have always used this potent metaphor to define their national identity. It demonstrates that America's 'redeemer myth' owes more to nineteenth- and twentieth-century reinventions of the Puritans than to the colonists' own conceptions of divine election. It reconstructs the complete story of 'the city on a hill' from its Puritan origins to the present day for the first time. From John Winthrop's 1630 'Model of Christian Charity' and the history books of the nineteenth century to the metaphor's sudden prominence in the 1960s and Reagan's skillful incorporation of it into his rhetoric in the 80s, 'the city on a hill' has had a complex history: this history reveals much about received notions of American exceptionalism, America's identity as a Christian nation, and the impact of America's civil religion. The conclusion considers the current status of 'the city on a hill' and summarizes what this story of national myth eclipsing biblical metaphor teaches us about the evolution of America's identity. 606 $aNational characteristics, American 607 $aUnited States$xCivilization 615 0$aNational characteristics, American. 676 $a974.402092 700 $aGamble$b Richard M.$0626206 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910788480503321 996 $aIn search of the city on a hill$93800712 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