M.Sc. Zhen Qian

I am currently a doctoral student in the Earth System Modelling group at TUM and a guest doctoral researcher at the Future Lab 'Artificial Intelligence in the Anthropocene' at PIK, supervised by Prof. Dr. Niklas Boers. Previously, I worked as a research assistant in the Open Geographic Modelling and Simulation (OpenGMS) group at Nanjing Normal University, under the supervision of Prof. Dr. Min Chen. For more details, please visit my homepage.

My research is driven by my passion for merging geospatial technologies, such as geoinformatics and remote sensing, with advanced data-driven approaches like machine learning and deep learning. I'm also interested in assessing the developed models beyond their accuracy, including generalisation, explainability, interpretability, and reproducibility. My overall goal is to use these interdisciplinary methodologies to explore and deepen our understanding of the interactions between human and Earth systems, contributing to their sustainability in the Anthropocene era. My academic journey can be summarised in two main areas of focus: (1) examining sustainable urban environments at various scales, encompassing infrastructure and landscapes, in response to climate change, and (2) examining the resilience of forests following anthropogenic disturbances (e.g., deforestation) and their interactions with climate change effects.

Selected ones (publication list is available via my Google Scholar profile)

Min Chen†, Zhen Qian†, Niklas Boers, Felix Creutzig, Gustau Camps-Valls, Klaus Hubacek, Christophe Claramunt et al.: Collaboration between artificial intelligence and Earth science communities for mutual benefit. Nature Geoscience, 17(10) (2024). https://doi.org/10.1038/s41561-024-01550-x († refers to equal contribution)

Zhen Qian, Min Chen, Zhuo Sun, Fan Zhang, Qingsong Xu, Jinzhao Guo, Zhiwei Xie, and Zhixin Zhang: Simultaneous extraction of spatial and attributional building information across large-scale urban landscapes from high-resolution satellite imagery. Sustainable Cities and Society, 105393 (2024). https://doi.org/10.1016/j.scs.2024.105393

Min Chen†, Zhen Qian†, Niklas Boers, Anthony J. Jakeman, Albert J. Kettner, Martin Brandt, Mei-Po Kwan et al.: Iterative integration of deep learning in hybrid Earth surface system modelling. Nature Reviews Earth & Environment, 4(8), 568-581 (2023). https://doi.org/10.1038/s43017-023-00452-7 († refers to equal contribution)

Zhen Qian, Min Chen, Yue Yang, Teng Zhong, Fan Zhang, Rui Zhu, Kai Zhang et al.: Vectorized dataset of roadside noise barriers in China using street view imagery. Earth System Science Data, 14(9), 4057-4076 (2022). https://doi.org/10.5194/essd-14-4057-2022

Zhen Qian, Min Chen, Teng Zhong, Fan Zhang, Rui Zhu, Zhixin Zhang, Kai Zhang, Zhuo Sun, and Guonian Lü: Deep Roof Refiner: A detail-oriented deep learning network for refined delineation of roof structure lines using satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 107, 102680 (2022). https://doi.org/10.1016/j.jag.2022.102680

Zhixin Zhang, Zhen Qian, Teng Zhong, Min Chen, Kai Zhang, Yue Yang, Rui Zhu et al.: Vectorized rooftop area data for 90 cities in China. Scientific Data, 9(1), 66 (2022). https://doi.org/10.1038/s41597-022-01168-x

Zhen Qian, Xintao Liu, Fei Tao, and Tong Zhou: Identification of urban functional areas by coupling satellite images and taxi GPS trajectories. Remote Sensing, 12(15), 2449 (2020). https://doi.org/10.3390/rs12152449