High Performance Spatial Computing for Big Data Analytics

Li Z., Huang Q., Jiang Y., Hu F., SOVAS: A Scalable Online Visual Analytic System for Big Climate Data Analysis, International Journal of Geographic Information Science (provisionally accepted)

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)

Article in review

Ning, H., Huang, X., Li, Z.,Wang, C., Xiang D., A rapid building change detection method based on UAV image, International Journal of Remote Sensing

Ning H., Li Z., Wang C., Leveraging Existing Land Cover Data to Build Deep Learning Training Set for Remote Sensing Image Classification, International Journal of Geo-information

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

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