LEADER 05768oam 2200781I 450 001 9910461826103321 005 20220111002408.0 010 $a1-136-91868-X 010 $a1-280-68159-4 010 $a1-136-91869-8 010 $a9786613658531 010 $a0-203-84423-8 024 7 $a10.4324/9780203844236 035 $a(CKB)2670000000208537 035 $a(EBL)956908 035 $a(OCoLC)798532439 035 $a(SSID)ssj0000686155 035 $a(PQKBManifestationID)11409903 035 $a(PQKBTitleCode)TC0000686155 035 $a(PQKBWorkID)10731273 035 $a(PQKB)10216893 035 $a(MiAaPQ)EBC956908 035 $a(PPN)242612326 035 $a(Au-PeEL)EBL956908 035 $a(CaPaEBR)ebr10610155 035 $a(CaONFJC)MIL365853 035 $a(OCoLC)815376155 035 $a(EXLCZ)992670000000208537 100 $a20180706d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 04$aThe Routledge handbook of hazards and disaster risk reduction /$f[edited by] Ben Wisner, J.C. Gaillard and Ilan Kelman 205 $aFirst edition. 210 1$aNew York :$cRoutledge,$d2012. 215 $a1 online resource (876 p.) 300 $aDescription based upon print version of record. 311 $a0-415-52325-7 311 $a0-415-59065-5 320 $aIncludes bibliographical references (p. 794-857) and index. 327 $aFront Cover; The Routledge Handbook of Hazards and Disaster Risk Reduction; Copyright Page; Contents; List of figures; List of tables; List of boxes; Author biographies; Foreword by Salvano Bricen?o; Acknowledgements; 1. Challenging risk: we offer the reader a left-foot book: The Editors; Part I: Big picture views - hazards, vulnerabilities and capacities; 2. Introduction to Part I: The Editors; 3. Framing disaster: theories and stories seeking to understand hazards, vulnerability and risk: Ben Wisner, JC Gaillard and Ilan Kelman; Politics, history and power 327 $a4. Historical concepts of disasters and risk: Greg Bankoff5. Politics: power and disasters: Adolfo Mascarenhas and Ben Wisner; 6. Human rights and disaster: Jean Connolly Carmalt and Claudine Haenni Dale; 7. Violent conflict, natural hazards and disaster: Ben Wisner; Culture, knowledge and religion; 8. Culture, hazard and disaster: Kenneth Hewitt; 9. Knowledge and disaster risk reduction: Jessica Mercer; 10. Religious interpretations of disaster: David Chester, Angus M. Duncan and Heather Sangster; 11. Hazards and disasters represented in film: Gregory Berger and Ben Wisner 327 $a12. Hazards and disasters represented in music: Bob AlexanderEnvironment, development and sustainability; 13. Hazards, risk and urbanisation: Mark Pelling; 14. Disaster risk and sustainable development: Christopher M. Bacon; Part II: Fine-grained views - hazards, vulnerabilities and capacities; 15. Introduction to Part II: The Editors; All hazards; 16. Data sources on hazards: Julio Serje; 17. Tools for identifying hazards: Keiko Saito, Jane Strachan, Timothy Fewtrell, Nick Rosser, Susanna Jenkins, Aidan Slingsby and Katharine Haynes; Hydro-meteorological/climatological hazards 327 $a18. Hazard, risk and climate change: David Simon19. Coastal storm: Sebastiaan N. Jonkman, Herman Gerritsen and Marcel Marchand; 20. Thunderstorm and tornado: David Etkin, Kaz Higuchi and George Platsis; 21. Flood: Hanna Schmuck; 22. Drought: Thomas A. Smucker; 23. Extreme heat and cold: Sabrina McCormick; 24. Wildfire: Roger Underwood and Alexander Held; Geophysical hazards; 25. Landslide and other mass movements: Danang Sri Hadmoko and Salvatore Engel-Di Mauro; 26. Earthquake: Cinna Lomnitz and Ben Wisner; 27. Tsunami: Brian G. McAdoo 327 $a28. Volcanic eruption: Susanna Jenkins and Katharine Haynes29. Soil erosion and contamination: Salvatore Engel-Di Mauro; Biological/ecological hazards; 30. Human epidemic: Chris Dibben; 31. Livestock epidemic: Delia Grace and John McDermott; 32. Plant disease, pests and erosion of biodiversity: Pascal O. Girot; Astronomical hazards; 33. Hazards from space: Bill McGuire; Vulnerabilities and capacities; 34. Disability and disaster: David Alexander, JC Gaillard and Ben Wisner; 35. Gender, sexuality and disaster: Maureen Fordham; 36. Children, youth and disaster: Agnes A. Babugura 327 $a37. Elderly people and disaster: Ehren B. Ngo 330 $aThe Handbook provides a comprehensive statement and reference point for hazard and disaster research, policy making, and practice in an international and multi-disciplinary context. It offers critical reviews and appraisals of current state of the art and future development of conceptual, theoretical and practical approaches as well as empirical knowledge and available tools.Organized into five inter-related sections, this Handbook contains sixty-five contributions from leading scholars. Section one situates hazards and disasters in their broad political, cultural, 606 $aNatural disasters$xRisk assessment$vHandbooks, manuals, etc 606 $aHazard mitigation$vHandbooks, manuals, etc 606 $aEmergency management$vHandbooks, manuals, etc 608 $aElectronic books. 615 0$aNatural disasters$xRisk assessment 615 0$aHazard mitigation 615 0$aEmergency management 676 $a363.34/6 676 $a363.346 676 $a363.347 701 $aGaillard$b J. C$0990289 701 $aKelman$b Ilan$0924141 701 $aWisner$b Ben$0299369 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910461826103321 996 $aThe Routledge handbook of hazards and disaster risk reduction$92265397 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