High Performance Spatial Computing and Deep Learning

Li Z., Tang W., Huang Q., Shook E., Guan Q., (2020), Introduction to Big Data Computing for Geospatial ApplicationsISPRS International Journal of Geo-Information, 9(8), 487; https://doi.org/10.3390/ijgi9080487

Li Z., (2020) Geospatial Big Data Handling with High Performance Computing: Current Approaches and Future Directions, In Tang, W., Wang, S., (eds.), High Performance Computing for Geospatial Applications (Springer)

Xu D., Huang X., Li Z., Li X. (2020), Local Motion Simulation using Deep Reinforcement LearningTransactions in GIS, https://doi.org/10.1111/TGIS.12620

Ning H., Li Z., Hodgson M., Wang C., (2020), Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning, ISPRS International Journal of Geo-Information, 9(2), 104; https://doi.org/10.3390/ijgi9020104

Ning H., Huang X., Li Z., Wang C., Xiang D., (2020) Detecting New Building Construction in Urban Areas Based on Images of Small Unmanned Aerial System, Papers in Applied Geography, https://doi.org/10.1080/23754931.2019.1707108

Huang X., Li Z., Wang C., Ning H., (2020),  Identifying disaster related social media for rapid response: a visual-textual fused CNN architectureInternational Journal of Digital Earth, 13(9), https://doi.org/10.1080/17538947.2019.1633425

Li Z., Gui Z, Hofer B., Li Y., Scheider S., Shekhar S., (2019) Geospatial Information Processing Technologies, In Guo, H., Goodchild, M.F., Annoni, A. (eds.), Manual of Digital Earth (Springer)

Li Z., Huang Q., Jiang Y., Hu F. (2020), SOVAS: A Scalable Online Visual Analytic System for Big Climate Data Analysis, International Journal of Geographic Information Science, 1-22, 10.1080/13658816.2019.1605073

Yang L., Sun X., Li Z. (2019)  An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology, Remote Sensing, 11(2), 152

Li Z., Hodgson M., Li W.  (2018) A general-purpose framework for large-scale Lidar data processing, International Journal of Digital Earth, 11(1), 26-47

Li Z., Huang Q., Carbone G., Hu F. (2017)  High Performance Query Analytical Framework for Supporting Data-intensive Climate Studies, Computers, Environment and Urban Systems, 62(3), 210-221

Li, Z., Hu, F., Schnase, J. L., Duffy, D. Q., Lee, T., Bowen, M. K., & Yang, C. (2017). A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduceInternational Journal of Geographical Information Science, 31(1), 17-35.

Gui, Z., Yu, M., Yang, C., Jiang, Y., Chen, S., Xia, J., Huang, Q., Liu, K., Li, Z., Hassan, M.A. and Jin, B., (2016). Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation. PloS one, 11(4), p.e0152250.

Li Z., Yang C., Yu M., Liu K., Sun M.(2015) Enabling Big Geoscience Data Analytics with a Cloud-based, MapReduce-enabled and Service-oriented Workflow FrameworkPloS one, 10(3), e0116781.

Yang, C., Sun, M., Liu, K., Huang, Q., Li, Z., Gui, Z., Jiang, Y., et al., (2014). Contemporary Computing Technologies for Processing Big Spatiotemporal Data. In Kwan M.P., Richardson D., Wang D., Zhou C.,(Eds.), Space-Time Integration in Geography and GIScience (pp. 327-351). Springer Netherlands.

Li Z., Yang C., Sun M., Li J., Xu C., Huang Q., & Liu K., (2013). A High Performance Web-Based System for Analyzing and Visualizing Spatiotemporal Data for Climate Studies. In W2GIS, Lecture Notes in Computer Science, Volume 7820 (pp. 190-198). Springer Berlin Heidelberg.

Yang C., Wu H., Huang Q., Li Z., and Li J., (2011). Using spatial principles to optimize distributed computing for enabling the physical science discoveriesProceedings of National Academy of Sciences, 108(14): 5498-5503 (spatial computing definition paper captured by Nobel Intent Blog)

Special Issue on Big Data Computing for Geospatial Applications” in the ISPRS International Journal of Geo-Information 

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