LEADER 00800nam0 2200277 450 001 9910289049003321 005 20181031115724.0 010 $a9789814425247 100 $a20181031d2013----km y0itay50 ba 101 0 $aeng 102 $aSG 105 $a 001yy 200 1 $aAlgorithmics of matching under preferences$fDavid F. Manlove$gKurt Mehlhorn 210 $aSingapore$cWord Scientific$d2013 215 $axxvi, 491 p.$d24 cm 225 1 $aTheoretical Computer Science$v2 700 1$aManlove,$bDavid$0759605 702 1$aMehlhorn,$bKurt 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910289049003321 952 $aVII-B-143$fMAS 952 $aVII-B-144$fMAS 959 $aMAS 996 $aAlgorithmics of matching under preferences$91535835 997 $aUNINA LEADER 00914nam a2200277 i 4500 001 991000946939707536 005 20020507104230.0 008 951219s1964 us ||| | eng 035 $ab10152052-39ule_inst 035 $aLE00639582$9ExL 040 $aDip.to Fisica$bita 084 $a53.3.12 084 $a53.3.16 084 $a530.1 084 $aQC794 100 1 $aGillespie, John$0462024 245 10$aFinal-state interactions /$cJohn Gillespie 260 $aSan Francisco :$bHolden-Day, Inc.,$c1964 300 $aviii, 104 p. :$bill. ;$c24 cm. 650 4$aNuclear reactions 907 $a.b10152052$b21-09-06$c27-06-02 912 $a991000946939707536 945 $aLE006 53.3.12+53.3.16 GIL$g1$i2006000049689$lle006$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10182226$z27-06-02 996 $aFinal-state interactions$9187896 997 $aUNISALENTO 998 $ale006$b01-01-95$cm$da $e-$feng$gus $h0$i1 LEADER 05004nam 22006735 450 001 9910734886003321 005 20230623134041.0 010 $a9789819930067 010 $a9819930065 024 7 $a10.1007/978-981-99-3006-7 035 $a(MiAaPQ)EBC30606127 035 $a(Au-PeEL)EBL30606127 035 $a(DE-He213)978-981-99-3006-7 035 $a(PPN)272264865 035 $a(CKB)27195929800041 035 $a(OCoLC)1386274737 035 $a(EXLCZ)9927195929800041 100 $a20230623d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Remote Sensing for Urban and Landscape Ecology /$fedited by Sk. Mustak, Dharmaveer Singh, Prashant Kumar Srivastava 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (326 pages) 225 1 $aAdvances in Geographical and Environmental Sciences,$x2198-3550 311 08$aPrint version: Mustak, Sk. Advanced Remote Sensing for Urban and Landscape Ecology Singapore : Springer,c2023 9789819930050 327 $aData And Urban Poverty: Detecting And Characterising Slums And Deprived Urban Areas In Low-And Middle-Income Countries -- Investigation Of Ecological Sustainability Through The Landscape Approach Of Geospatial Technology: Study From New Town Project In Eastern India -- Advanced Remote Sensing For Sustainable Decent Housing For The Economically Challenged Urban Households -- Impact Of Uncontrolled Tourism Development On Landscape Ecology Of Purba Medinipur Coastal Region, West Bengal: A 4-C Framework And Swoc Analysis -- Impact Of Urban Heat Island: A Local-Level Urban Climate Phenomenon On Urban Ecology And Human Health -- Identification Of Environmental Epidemiology Through Advanced Remote Sensing Based On Ndvi -- Assessment Of Land Utilization Pattern And Their Relationship With Surface Temperature And Vegetation In Sikkim, India -- Monitoring Land Use And Land Cover Change Over Bhiwani District Using Google Earth Engine -- Image And Perception Of Royal Heritage And Eco-Space Of The Medium Towns In India: Reflection From Burdwan Royal Heritage Site -- Governance And Floodplain Extent Changes Of Yamuna River Floodplain In Megacity Delhi -- Assessing Urban Compactness Using Machine Learning And Earth Observation Datasets: A Case Study Of Kolkata City -- Analysis Of Ecological Vulnerability Behind The Land Conversion From Agriculture To Aquaculture Of Purba Medinipur District In West Bengal, India -- Environmental Change Analysis Using Remote Sensing And Gis: A Study Of Upper Baitarani Basin, Odisha -- Mapping Urban Footprint Using Machine Learning And Public Domain Datasets. 330 $aThis book introduces the use of various remote sensing data such as microwave, hyperspectral and very high-resolution (VHR) satellite imagery; mapping techniques including pixel and object-based machine learning; and geostatistical modelling techniques including cellular automation, entropy and land fragmentation. Remote sensing plays a vital role in solving urban and environmental challenges at the landscape level. Globally, more than half of the urban population is facing severe environmental and social challenges, especially those relating to climate change, agricultural land encroachment, green infrastructure and environmental degradation, mobility due to rapid rural?urban transformation and anthropogenic interventions. Mapping and quantification of such threats at the landscape level are challenging for experts using traditional techniques; however, remote sensing technology provides diverse spatial data at a varying scale, volume and accessibility for mapping and modelling, and it also analyses challenges at urban and landscape levels. Together, they address challenges at urban and landscape levels to support the Sustainable Development Goals (SDGs). 410 0$aAdvances in Geographical and Environmental Sciences,$x2198-3550 606 $aGeographic information systems 606 $aCartography 606 $aUrban ecology (Biology) 606 $aHuman geography 606 $aGeographical Information System 606 $aCartography 606 $aUrban Ecology 606 $aHuman Geography 615 0$aGeographic information systems. 615 0$aCartography. 615 0$aUrban ecology (Biology) 615 0$aHuman geography. 615 14$aGeographical Information System. 615 24$aCartography. 615 24$aUrban Ecology. 615 24$aHuman Geography. 676 $a910.285 700 $aMustak$b Sk$01372647 701 $aSingh$b Dharmaveer$01372648 701 $aSrivastava$b Prashant Kumar$01372649 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734886003321 996 $aAdvanced Remote Sensing for Urban and Landscape Ecology$93403542 997 $aUNINA