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The automated man-made object detection and building extraction from single satellite images is, still, one of the most challenging task for various urban planning and monitoring engineering applications. To this end, in this paper we propose an automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks. The core of the developed method is based on a supervised classification procedure employing a very large training dataset.doi:10.1109/igarss.2015.7326158 dblp:conf/igarss/VakalopoulouKKP15 fatcat:7grqlipjtbby5lvkv4sry23dwy