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• W3136264809 abstract "With increase in urbanization and Earth Sciences research into urban areas, the need to quickly and accurately segment urban rooftop maps has never been greater. Current machine learning techniques struggle to produce high accuracy maps in dense urban zones where there is high image noise and foot print overlap. In this paper, we evaluate a training methodology for pixel-wise segmentation for high-resolution satellite imagery using progressive growing of generative adversarial networks as a solution. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We evaluate our approach using the SpaceNet version 2 and xView datasets. Our experiments show that for SpaceNet, progressive Generative Adversarial Network (GAN) training achieved a test accuracy of 93% compared to 89% for traditional GAN training and 87% for U-Net architecture, while for xView, we achieved 71% accuracy using progressive GAN training compared to 69% through traditional GAN training and 65% using U-Net." @default.
• W3136264809 created "2021-03-29" @default.
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• W3136264809 date "2021-03-22" @default.
• W3136264809 modified "2023-09-27" @default.
• W3136264809 title "Semantic Segmentation of High Resolution Satellite Imagery using Generative Adversarial Networks with Progressive Growing" @default.
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• W3136264809 doi "https://doi.org/10.1080/2150704x.2021.1895444" @default.
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