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Prof. Qiming Zhou

College of Surveying and Geo-informatics, Tongji University, Shanghai, China

Email: Qmz852@outlook.com

Prof. Qiming Zhou is a Professor at College of Surveying and Geo-informatics, Tongji University, Shanghai, China. He also holds the posts of Professor Emeritus at Department of Geography, Hong Kong Baptist University (HKBU), Guest Professor at National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University (WHU), and Research Fellow at HKBU Shenzhen Institute of Research and Continuing Education (IRACE). His research interests cover a broad area of geo-spatial information science, particularly in geo-computation and remote sensing applications. He has been actively engaged in research such as digital terrain analysis, climate change and its impacts on regional and global ecosystems, landuse and land cover change detection, and GIS and remote sensing applications to urban, environment and natural resource management. He has published 2 books and over 150 research papers (about 100 of them are indexed by SCI/SSCI). By the end of 2024, his h-index was 41 (Google) with over 6000 citations.

Geo-smartness in Urban Functional Zone Recognition Supported by Multi-modal Geospatial Data

Urban Functional Zones (UFZs), as fundamental spatial units for urban governance and planning, emerge from the complex interplay between anthropogenic activities and natural ecosystems, exhibiting heterogeneous morphological characteristics across urban landscapes. As critical carriers for addressing urbanization challenges, these functional units confront unprecedented pressures from rapid urban sprawl and population agglomeration, while simultaneously revealing systemic deficiencies in land use efficiency that critically constrain sustainable urban development. Accurate delineation and dynamic monitoring of UFZs' compositional patterns and spatial configurations have become imperative prerequisites for formulating evidence-based land use optimization strategies.
The proliferation of geospatial big data, propelled by advancements in Internet of Things (IoT) and ubiquitous computing technologies, has catalyzed paradigm shifts in geo-intelligent UFZ recognition. Multi-modal data sources, including high-resolution remote sensing (HRS) imagery, open-source mapping databases, and location-aware social media streams, collectively establish an enriched embedding space that synergistically integrates physical, spatial-temporal, and socio-semantic urban representations. This technological confluence, augmented by breakthroughs in geographic information science and spatial positioning systems, has engendered novel analytical capabilities for urban morphology characterization. However, current methodologies still exhibit limitations in UFZs spatial modeling: inadequate hierarchical relationship modeling across heterogeneous geographic entities (points, polylines, polygons), underdeveloped cross-modal data synergy between vector and raster formats. 
To address these gaps, this study developed a comprehensive framework based on data prerequisites in downstream urban applications, providing solutions for three scenarios: 1) leveraging open-source vector mapping data through an indicator system modeling hierarchical geometric primitive relationships between point- and polygon-based entities, with polyline-based geo-entities generating block-level research units; 2) utilizing remote sensing raster images via a joint model integrating object detection and relationship modelling modules; 3) integrating vector and raster datasets through a two-stage model employing self-supervised embedding learning and downstream fine-tuning strategies. This framework enables timely delineation of fine-grained UFZs with high accuracy, and provides a comprehensive approach to UFZ mapping that addresses multi-modal data challenges while preserving geographical entity relationships.

 

Publications (since 2022) (* corresponding author)

ZHU, Honglin; ZHOU, Qiming*; KRISP, Jukka M.; in press, Exploring machine learning approaches for precipitation downscaling, Geo-spatial Information Science, doi: 10.1080/10095020.2025.2477547. [SCI] Q1

DU, Zhuotong; SUI, Haigang*; ZHOU, Qiming; ZHOU, Mingting; SHI, Weiyue; WANG, Jianxun; LIU, Junyi; 2024, Vectorized building extraction from high-resolution remote sensing images using spatial cognitive graph convolution model, 9 May (July) 2024, ISPRS Journal of Photogrammetry and Remote Sensing, 213: 53-71, doi: 10.1016/j.isprsjprs.2024.05.015. [SCI] Q1

CUI, Aihong, LI, Jianfeng, ZHOU, Qiming*, ZHU, Honglin, LIU, Huizeng, YANG, Chao, WU, Guofeng, LI, Qingquan, 2024, Propagation dynamics from meteorological drought to GRACE-based hydrological drought and its influence factors, 10 March 2024, Remote Sensing, 16(6): 976, doi: 10.3390/rs16060976. [SCI] Q1

ZHU, Honglin, ZHOU, Qiming*, 2024, Advancing satellite-derived precipitation downscaling in data-sparse area through deep transfer learning, 19 February 2024, IEEE Transactions on Geoscience and Remote Sensing, 62: 1-13, doi: 10.1109/TGRS.2024.3367332. [SCI] Q1

LI, Jianfeng, WU, Ka Wai, ZHOU, Qiming, 2024, Enhancing students’ field experience in physical geography courses using virtual reality technology, in Moorhouse, B.L., Li, S.S.C., Pahs, S. eds., Teaching with Technology in the Social Sciences, Springer, 81-87, ISBN 978-981-99-8418-3.

ZHU, Honglin, LIU, Huizeng, ZHOU, Qiming*, CUI, Aihong, 2023, A XGBoost-based downscaling-calibration scheme for extreme precipitation events, IEEE Transactions on Geoscience and Remote Sensing, July 2023, 61, 4103512, doi: 10.1109/TGRS.2023.3294266. [SCI] Q1

ZHU, Honglin, LIU, Huizeng, ZHOU, Qiming*, CUI, Aihong, 2023, Towards an accurate and reliable downscaling scheme for high-spatial-resolution precipitation data, Remote Sensing, May 2023, 15, 2640, doi: 10.3390/rs15102640. [SCI] Q1

Hu, Z., Chen, D., Chen, X.*, Zhou, Q., Peng, Y., Li, J., Sang, Y., 2022, CCHZ-DISO: a timely new assessment system for data quality or model performance from Da Dao Zhi Jian, 24 November 2022, Geophysical Research Letters, 49, e2022GL100681, doi: 10.1029/2022GL100681. [SCI] Q1.

Sun, B., Zhang, Y., Zhou, Q.*, Zhang, X., 2022, Effectiveness of semi-supervised learning and multi-source data in detailed urban landuse mapping with a few labeled samples, Remote Sensing, 14: 648, doi: 10.3390/rs14030648. [SCI] Q1.

Liu, H., He, X., Li, Q., Hu, X., Ishizaka, J., Kratzer, S., Yang, C., Shi, T., Hu, S., Zhou, Q., Wu, G.*, 2022, Evaluation of ocean colour atmospheric correction methods for Sentinel-3 OLCI using global automatic in-situ observations, IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3136243. [SCI] Q1

Zhou, Q., Huang, J., Hu, Z.*, Yin, G., 2022, Spatial-temporal changes to GRACE-derived terrestrial water storage in response to climate change in arid Northwest China, Hydrological Sciences Journal, 67(4): 535-549, doi: 10.1080/02626667.2022.2030060, [SCI] Q1.

Hu, Z., Chen, X*., Zhou, Q., Yin, G., Liu, J., 2022, Dynamical variations of the terrestrial water cycle components and the influences of the climate factors over the Aral Sea Basin through multiple datasets, Journal of Hydrology, 604, 127270, doi: 10.1016/j.jhydrol.2021.127270 [SCI] Q1.

Editorship of Academic/Professional Journals

    • Editorial Board Member, Journal of Aridland, Science Press
    • Editorial Board Member, ISPRS International Journal of Geo-Information, ISPRS, Switzerland
    • Editorial Board Member, Geo-spatial Information Science, Taylor & Francis
    • Editorial Board Member, Journal of Geo-information Science, Science Press (Chinese)