High‐resolution quantification of building stock using multi‐source remote sensing imagery and deep learning
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DOI: 10.1111/jiec.13356
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- Mingming Hu & Ester Van Der Voet & Gjalt Huppes, 2010. "Dynamic Material Flow Analysis for Strategic Construction and Demolition Waste Management in Beijing," Journal of Industrial Ecology, Yale University, vol. 14(3), pages 440-456, June.
- Hattori, Ryoma & Horie, Sadataka & Hsu, Feng-Chi & Elvidge, Chirstopher D. & Matsuno, Yasunari, 2014. "Estimation of in-use steel stock for civil engineering and building using nighttime light images," Resources, Conservation & Recycling, Elsevier, vol. 83(C), pages 1-5.
- Rebecca K. Runting & Stuart Phinn & Zunyi Xie & Oscar Venter & James E. M. Watson, 2020. "Opportunities for big data in conservation and sustainability," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
- Christopher Yeh & Anthony Perez & Anne Driscoll & George Azzari & Zhongyi Tang & David Lobell & Stefano Ermon & Marshall Burke, 2020. "Using publicly available satellite imagery and deep learning to understand economic well-being in Africa," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
- Sun, Maoran & Han, Changyu & Nie, Quan & Xu, Jingying & Zhang, Fan & Zhao, Qunshan, 2022. "Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow," OSF Preprints g8p4f, Center for Open Science.
- Li, Xiaoma & Zhou, Yuyu & Yu, Sha & Jia, Gensuo & Li, Huidong & Li, Wenliang, 2019. "Urban heat island impacts on building energy consumption: A review of approaches and findings," Energy, Elsevier, vol. 174(C), pages 407-419.
- Huang, Tao & Shi, Feng & Tanikawa, Hiroki & Fei, Jinling & Han, Ji, 2013. "Materials demand and environmental impact of buildings construction and demolition in China based on dynamic material flow analysis," Resources, Conservation & Recycling, Elsevier, vol. 72(C), pages 91-101.
- Kimberlee A. Marcellus-Zamora & Patricia M. Gallagher & Sabrina Spatari & Hiroki Tanikawa, 2016. "Estimating Materials Stocked by Land-Use Type in Historic Urban Buildings Using Spatio-Temporal Analytical Tools," Journal of Industrial Ecology, Yale University, vol. 20(5), pages 1025-1037, October.
- Ziqi Tang & Kangway V. Chuang & Charles DeCarli & Lee-Way Jin & Laurel Beckett & Michael J. Keiser & Brittany N. Dugger, 2019. "Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
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