Qingfeng Guan
China University of Geosciences (Wuhan)
162 Papers
209 Citations
Qingfeng Guan is an academic researcher from China University of Geosciences (Wuhan). The author has contributed to research in topics: Computer science & Urban planning. The author has an hindex of 18, co-authored 117 publications. Previous affiliations of Qingfeng Guan include Wuhan University & University of Nebraska–Lincoln.
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Papers
Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China
TL;DR: In this article, a patch-generating land use simulation (PLUS) model is proposed that integrates a land expansion analysis strategy and a cellular automata model based on multi-type random patch seeds to understand the drivers of land expansion and investigate the landscape dynamics in Wuhan, China.
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Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China
TL;DR: A patch-generating land use simulation (PLUS) model that integrates a land expansion analysis strategy and a CA model based on multi-type random patch seeds is introduced that can help policymakers to manage future land use dynamics and so to realize more sustainable land use patterns for future development.
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Climate variability has a stabilizing effect on the coexistence of prairie grasses
Peter B. Adler,Janneke HilleRisLambers,Phaedon C. Kyriakidis,Qingfeng Guan,Jonathan M. Levine +4 more
TL;DR: Analysis of three decades of demographic data from a Kansas prairie shows that interannual climate variability promotes the coexistence of three common grass species, suggesting that coexistence based on the storage effect may be underappreciated and could provide an important alternative to recent neutral theories of diversity.
A human-machine adversarial scoring framework for urban perception assessment using street-view images
Yao Yao,Yao Yao,Zhaotang Liang,Zehao Yuan,Penghua Liu,Yongpan Bie,Jinbao Zhang,Jinbao Zhang,Ruoyu Wang,Ruoyu Wang,Jiale Wang,Qingfeng Guan +11 more
TL;DR: A human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores is described, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities.
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Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery
TL;DR: Wang et al. as discussed by the authors proposed a Siamese global learning (Siam-GL) framework, which is a novel semantic change detction framework for HSR remote sensing images.
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