Publications

Collaboration between artificial intelligence and Earth science communities for mutual benefit
Artificial intelligence is rapidly being integrated into Earth science, but how Earth science may benefit artificial intelligence has been overlooked. We call for mutual balancing between the two disciplines and improving cross-disciplinary collaboration.
Min Chen, Zhen Qian, Niklas Boers, Felix Creutzig, Gustau Camps-Valls, Klaus Hubacek, Christophe Claramunt, John P. Wilson, Stefano Nativi, Anthony J. Jakeman, R. Dietmar Müller, Michael Batty, Chenghu Zhou, Fahu Chen, Qiao Wang, Fan Zhang, C. Michael Barton, Josef Strobl, Michael Meadows, Carlo Ratti, Philipp Hess, Qingsong Xu, Zhixin Zhang, Qiushi Gu, A-Xing Zhu, Hui Lin, Linwang Yuan, Guonian Lü
Collaboration between artificial intelligence and Earth science communities for mutual benefit
Simultaneous extraction of spatial and attributional building information across large-scale urban landscapes from high-resolution satellite imagery
To conduct a thorough examination of building infrastructure and develop a deeper understanding of human-urban dynamics, this study introduced a multi-task network, MT-BR, to extract unified building information. This study proposed a method for selecting representative samples for robust network training that considers both their location and the features of the urban environment they represent, so that they can be used effectively across large urban landscapes.
Zhen Qian, Min Chen, Zhuo Sun, Fan Zhang, Qingsong Xu, Jinzhao Guo, Zhiwei Xie, Zhixin Zhang
Simultaneous extraction of spatial and attributional building information across large-scale urban landscapes from high-resolution satellite imagery
Iterative integration of deep learning in hybrid Earth surface system modelling
Methods to integrate Earth system modelling (ESM) with deep learning offer promise for advancing understanding of Earth processes. This Perspective explores the development and applications of hybrid Earth system modelling, a framework that integrates neural networks into ESM throughout the modelling lifecycle.
Min Chen, Zhen Qian, Niklas Boers, Anthony J. Jakeman, Albert J. Kettner, Martin Brandt, Mei-Po Kwan, Michael Batty, Wenwen Li, Rui Zhu, Wei Luo, Daniel P. Ames, C. Michael Barton, Susan M. Cuddy, Sujan Koirala, Fan Zhang, Carlo Ratti, Jian Liu, Teng Zhong, Junzhi Liu, Yongning Wen, Songshan Yue, Zhiyi Zhu, Zhixin Zhang, Zhuo Sun, Jian Lin, Zaiyang Ma, Yuanqing He, Kai Xu, Chunxiao Zhang, Hui Lin, Guonian Lü
Iterative integration of deep learning in hybrid Earth surface system modelling
Vectorized dataset of roadside noise barriers in China using street view imagery
Roadside noise barriers (RNBs) are important urban infrastructures to ensure a city is liveable. This study provides the first reliable and nationwide vectorized RNB dataset with street view imagery in China. The generated RNB dataset is evaluated in terms of two aspects, i.e., the detection accuracy and the completeness and positional accuracy. The method is based on a developed geospatial artificial intelligence framework.
Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang, Zhixin Zhang, Zhuo Sun, Peilong Ma, Guonian Lü, Yu Ye, Jinyue Yan
Vectorized dataset of roadside noise barriers in China using street view imagery
Vectorized rooftop area data for 90 cities in China
The first rooftop area dataset for 90 cities in China is of high quality and can be used for data support and decision-making that can facilitate sustainable urban development effectively.
Zhixin Zhang, Zhen Qian, Teng Zhong, Min Chen, Kai Zhang, Yue Yang, Rui Zhu, Fan Zhang, Haoran Zhang, Fangzhuo Zhou, Jianing Yu, Bingyue Zhang, Guonian Lü, Jinyue Yan
Vectorized rooftop area data for 90 cities in China
Deep Roof Refiner: A detail-oriented deep learning network for refined delineation of roof structure lines using satellite imagery
To overcome these two challenges of delineating roof structure lines from satellite imagery, the Deep Roof Refiner—a detail-oriented deep learning network and a synthetic strategy were proposed.
Zhen Qian, Min Chen, Teng Zhong, Fan Zhang, Rui Zhu, Zhixin Zhang, Kai Zhang, Zhuo Sun, Guonian Lü
Deep Roof Refiner: A detail-oriented deep learning network for refined delineation of roof structure lines using satellite imagery
Identification of urban functional areas by coupling satellite images and taxi GPS trajectories
This paper provides new insights into how the advantages of satellite images and taxi trajectories in UFA identification can be fully exploited to support sustainable city management.
Zhen Qian, Xintao Liu, Fei Tao, Tong Zhou
Identification of urban functional areas by coupling satellite images and taxi GPS trajectories