Annex 17 : deep semantic segmentation for built-up area extraction and mapping from satellite imagery
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2018-03
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Abstract
Research 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.
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URBAN PLANNING, BUILT-UP AREAS, DEEP LEARNING, BIG DATA, MAPPING, SATELLITE IMAGES, MACHINE LEARNING, MACHINE READABLE, REMOTE SENSING, URBAN GROWTH, GLOBAL SOUTH, SRI LANKA, SOUTH ASIA