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Tracking the industrial growth of modern China with high-resolution panchromatic imagery: A sequential convolutional approach

作者: Ethan Brewer,Zhonghui Lv,Dan Runfola

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Due to insufficient or difficult to obtain data on development in inaccessible regions, remote sensing data is an important tool for interested stakeholders to collect information on economic growth. To date, no studies have utilized deep learning to estimate industrial growth at the level of individual sites. In this study, we harness high-resolution panchromatic imagery to estimate development over time at 419 industrial sites in the People's Republic of China using a multi-tier computer vision framework. We present two methods for approximating development

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本作品采用 知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可,转载请附上原文出处链接和本声明。
本文链接地址:https://flyai.com/paper_detail/15644
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