Annex 17 : deep semantic segmentation for built-up area extraction and mapping from satellite imagery

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dc.contributor.authorAthuraliya, C. D.
dc.contributor.authorRamasinghe, Sameera
dc.contributor.authorLokanathan, Sriganesh
dc.date.accessioned2018-03-29T15:36:35Z
dc.date.available2018-03-29T15:36:35Z
dc.date.issued2018-03
dc.description.abstractResearch focuses on generating more usable built-up area maps, as traditional methods (such as surveys and census) are infrequent and costly. The work proposes a modified Fully Convolutional Network (FCN) architecture that will improve semantic segmentation operation on satellite imagery for built-up area extraction and urban mapping. This method could bridge the gap between existing extraction techniques and actual land cover/built-up area maps used by practitioners. Applications are potentially to socio-economic classification and urban planning, where building density functions as a proxy measure for socio-economic level, and building distribution for urban area estimates and growth, respectively.en
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10625/56919
dc.language.isoen
dc.subjectURBAN PLANNINGen
dc.subjectBUILT-UP AREASen
dc.subjectDEEP LEARNINGen
dc.subjectBIG DATAen
dc.subjectMAPPINGen
dc.subjectSATELLITE IMAGESen
dc.subjectMACHINE LEARNINGen
dc.subjectMACHINE READABLEen
dc.subjectREMOTE SENSINGen
dc.subjectURBAN GROWTHen
dc.subjectGLOBAL SOUTHen
dc.subjectSRI LANKAen
dc.subjectSOUTH ASIAen
dc.titleAnnex 17 : deep semantic segmentation for built-up area extraction and mapping from satellite imageryen
dc.typeWorking Paperen
idrc.dspace.accessOpen Accessen
idrc.project.componentnumber108008001
idrc.project.number108008
idrc.project.titleLeveraging Mobile Network Big Data for Developmental Policyen
idrc.rims.adhocgroupIDRC SUPPORTEDen

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Annex 17: Deep Semantic Segmentation for Built-up Area Extraction and Mapping from Satellite Imagery
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