Athuraliya, C. D.Ramasinghe, SameeraLokanathan, Sriganesh2018-03-292018-03-292018-03http://hdl.handle.net/10625/56919Research 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.application/pdfenURBAN PLANNINGBUILT-UP AREASDEEP LEARNINGBIG DATAMAPPINGSATELLITE IMAGESMACHINE LEARNINGMACHINE READABLEREMOTE SENSINGURBAN GROWTHGLOBAL SOUTHSRI LANKASOUTH ASIAWorking Paper