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时空大数据：时空大数据分析、挖掘、模拟与可视化
空间计算智能：基于机器学习/人工智能的空间分析与时空模拟，解决复杂地理空间问题
高性能地理空间计算：利用并行计算技术，突破由大数据和复杂算法造成的地理空间计算壁垒
空间感知与计算：利用多源/多元数据，智能感知空间环境，分析和模拟空间环境变化，应用包括城市、土地利用与土地覆盖、地质、环境等
- 混合功能用地结构变化驱动的城市热岛强度空间格局推演方法及高性能计算框架,国家自然科学基金
- 基于高性能计算和深度学习的大尺度地区违法用地监测模拟和评估预警
- 基于GPU/CPU异构集群计算技术的高性能土地利用时空动态模拟研究,高等学校博士学科点专项科研基金项目
- 深部矿产资源评价理论与方法,国家科技重大专项
- 并行地理元胞自动机的计算强度时空变化模式及动态负载均衡方法研究,国家自然科学基金
- Big Data Computing for Geospatial Applications
- High-Performance GeoComputation with the Parallel Raster Processing Library. In: Thill JC., Dragicevic S. (eds) GeoComputational Analysis and Modeling of Regional Systems. Advances in Geographic Information Science
- Opportunities and Challenges for Urban Land-use and Land-cover Change Modeling in High-performance Computing. In: Shi, X. et al. (eds), Modern Accelerator Technologies for Geographic Information Science
- 融合多源时空数据的城市空气质量精细化估测方法及装置
- 一种基于射线计算单元的移动立方体并行计算方法及系统
- 基于空间约束多自编码器的多元地球化学异常识别方法
- 基于多卷积自编码神经网络的多元化探异常识别办法
- Li, Z.*; Tang, W.; Huang, Q.; Shook, E. & Guan, Q. (2020). Introduction to Big Data Computing for Geospatial Applications. ISPRS International Journal of Geo-Information (SCI), 9(8): 487. DOI: 10.3390/ijgi9080487
- Liang, X.; Guan, Q.*; Clarke, K. C.; Chen, G.; Guo, S. & Yao, Y. (2021). Mixed-cell cellular automata: A new approach for simulating the spatio-temporal dynamics of mixed land use structures. Landscape and Urban Planning (SCI/SSCI). DOI: 10.1016/j.landurbplan.2020.103960
- Zhu, Q.; Li, Z.; Zhang, Y. & Guan, Q.* (2020). Building Extraction from High Spatial Resolution Remote Sensing Images via Multiscale-Aware and Segmentation-Prior Conditional Random Fields. Remote Sensing (MDPI, SCI), 2020(12): 3983. DOI: 10.3390/rs12233983
- Liu, S.; Yang, X.; Guan, Q.; Lu, Z. & Lu, J.* (2020). An Ensemble Modeling Framework for Distinguishing Nitrogen, Phosphorous and Potassium Deficiencies in Winter Oilseed Rape (Brassica napus L.) Using Hyperspectral Data. Remote Sensing (MDPI, SCI), 2020(12): 4060. DOI: 10.3390/rs12244060
- Yao, Y; Liu, Y.; Guan, Q.*; Hong, Y.*; Wang, R.; Wang R. & Liang, X. (2021). Spatiotemporal distribution of human trafficking in China and predicting the locations of missing persons. Computers, Environment and Urban Systems (SSCI). DOI: 10.1016/j.compenvurbsys.2020.101567