LEADER 01240nam0 22002893i 450 001 SUN0125625 005 20191126115029.580 010 $a978-88-916314-0-4$d0.00 100 $a20191126d2018 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aIl *sistema pavimento$epavimentazioni in legno abbinate ai sistemi radianti$fa cura di Clara Peretti e Pietro Belloni$gcon i contributi di Daniele Agostinetto ... [et al.] 210 $aSantarcangelo di Romagna$cMaggioli$d2018 215 $a156 p.$cill.$d24 cm. 410 1$1001SUN0026986$12001 $aProgettazione: tecniche & materiali$v116$1210 $aRimini$cMaggioli. 606 $aImpianti di riscaldamento$2AR$3SUNC035455 620 $dSantarcangelo di Romagna$3SUNL000123 702 1$aPeretti$b, Clara$3SUNV097048 702 1$aBelloni$b, Pietro$3SUNV097049 702 1$aAgostinetto$b, Daniele$3SUNV097050 712 $aMaggioli$3SUNV000144$4650 801 $aIT$bSOL$c20200309$gRICA 912 $aSUN0125625 950 $aBIBLIOTECA DEL DIPARTIMENTO DI ARCHITETTURA E DISEGNO INDUSTRIALE$d01PREST IIEb131 $e01BDA1336 20191126 996 $aSistema pavimento$91566675 997 $aUNICAMPANIA LEADER 01274nam a2200289 i 4500 001 991001258649707536 005 20020502185821.0 008 960321s1989 it ||| | ita 020 $a8885857116 035 $ab1148648x-39ule_inst 035 $aPRUMB57676$9ExL 040 $aScuola per assistenti sociali$bita 082 0 $a361.4 100 1 $aMaguire, Lambert$0527437 245 13$aIl lavoro sociale di rete :$bl'operatore sociale come mobilizzatore e coordinatore delle risorse informali della comunità /$cLambert Maguire ; traduzione, introduzione, cura di Fabio Folgheraiter 260 $aTrento :$bCentro studi Erickson,$c1989 300 $a135 p. ;$c24 cm. 500 $aTrad. di: Understanding social networks. 650 4$aOperatori sociali - Lavoro di gruppo 700 1 $aFolgheraiter, Fabio 907 $a.b1148648x$b02-04-14$c01-07-02 912 $a991001258649707536 945 $aLE024 SS/A II 7$g1$i2021000183935$lle021$nex DUSS$o-$pE0.00$q-$rl$s- $t0$u4$v0$w4$x0$y.i11677910$z01-07-02 945 $aLE024 SS/A III 29$g1$i2021000216480$iLE024N-3680$lle021$nex DUSS$o-$pE0.00$q-$rl$s- $t0$u2$v0$w2$x0$y.i11677922$z01-07-02 996 $aLavoro sociale di rete$9812851 997 $aUNISALENTO 998 $ale021$b01-01-96$cm$da $e-$fita$git $h3$i2 LEADER 04240nam 22007095 450 001 9910588591603321 005 20251113200340.0 010 $a981-19-3739-7 024 7 $a10.1007/978-981-19-3739-2 035 $a(MiAaPQ)EBC7076036 035 $a(Au-PeEL)EBL7076036 035 $a(CKB)24723841600041 035 $a(PPN)264197143 035 $a(OCoLC)1342593577 035 $a(DE-He213)978-981-19-3739-2 035 $a(EXLCZ)9924723841600041 100 $a20220818d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRemote Sensing Intelligent Interpretation for Mine Geological Environment $eFrom Land Use and Land Cover Perspective /$fby Weitao Chen, Xianju Li, Lizhe Wang 205 $a1st ed. 2022. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2022. 215 $a1 online resource (254 pages) 225 1 $aEarth and Environmental Science Series 311 08$aPrint version: Chen, Weitao Remote Sensing Intelligent Interpretation for Mine Geological Environment Singapore : Springer,c2022 9789811937385 327 $aPreface.-Mine geological environment: An overview.-Multimodal remote sensing science and technology.-Deep learning technology for remote sensing intelligent interpretation.-Remote sensing interpretation signs of mine land occupation type -- Mine remote sensing dataset construction for multi-level tasks -- Mine target detection by remote sensing and deep learning -- Mine remote sensing scene classification by deep learning -- Mine land occupation classification based on machine learning and remote sensing images -- Mine land occupation classification based on deep learning and remote sensing images -- Concluding remarks. 330 $aThis book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of ?target detection?scene classification?semantic segmentation." Taking China?s Hubei Province as an example, this book focuses on the following four aspects: 1. Development of a multiscale remote sensing dataset of the mining area, including mine target remote sensing dataset, mine (including non-mine areas) remote sensing scene dataset, and semantic segmentation remote sensing dataset of mining land cover. The three datasets are the basis of intelligent interpretation based on deep learning. 2. Research on mine target remote sensing detection method based on deep learning. 3. Research on remote sensing scene classification method of mine and non-mine areas based on deep learning. 4. Research on the fine-scale classification method of mining land cover based on semantic segmentation. The book is a valuable reference both for scholars, practitioners and as well as graduate students who are interested in mining environment research. 410 0$aEarth and Environmental Science Series 606 $aGeographic information systems 606 $aMachine learning 606 $aSignal processing 606 $aGeology 606 $aEnvironmental monitoring 606 $aGeographical Information System 606 $aMachine Learning 606 $aSignal, Speech and Image Processing 606 $aGeology 606 $aEnvironmental Monitoring 615 0$aGeographic information systems. 615 0$aMachine learning. 615 0$aSignal processing. 615 0$aGeology. 615 0$aEnvironmental monitoring. 615 14$aGeographical Information System. 615 24$aMachine Learning. 615 24$aSignal, Speech and Image Processing. 615 24$aGeology. 615 24$aEnvironmental Monitoring. 676 $a006.31 700 $aChen$b Weitao$01254062 702 $aLi$b Xianju 702 $aWang$b Lizhe$f1974- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910588591603321 996 $aRemote sensing intelligent interpretation for mine geological environment$93363944 997 $aUNINA