GeoAI and High Performance Spatial Computing

Kazemi M., Weng Q., Haghi V., Li Z., Arsanjani J., (2022). Learning-based methods for detection and monitoring of shallow flood-affected areas: Impact of shallow-flood spreading on vegetation density, Canadian Journal of Remote Sensing,

Ning H., Li Z., Wang C., Hodgson M., Huang X., Li X., (2022) Converting street view images to land cover maps for metric mapping: a case study on sidewalk network extraction for the wheelchair usersComputers, Environment and Urban Systems.

Kazemi-Garajeh M., Blaschke T., Haghi V., Weng Q., Kamran K., Li Z., (2022). A comparison between Sentinel-2 and Landsat 8 OLI satellite images for soil salinity distribution mapping using a deep learning convolutional neural networkCanadian Journal of Remote Sensing.

Morgan G., Wang C., Li Z., Schill S., Morgan D., Deep Learning of High-Resolution Aerial Imagery for Coastal Marsh Change Detection: A Comparative StudyISPRS International Journal of Geo-Information,

Song, Y., Ning, H., Ye, X., Chandana, D., & Wang, S. (2022). Analyze the usage of urban greenways through social media images and computer visionEnvironment and Planning B: Urban Analytics and City Science, 23998083211064624.

Li, X., Ning, H., Huang, X., Dadashova, B., Kang, Y., & Ma, A. (2022). Urban infrastructure audit: an effective protocol to digitize signalized intersections by mining street view images. Cartography and Geographic Information Science, 49(1), 32-49.

Ning H., Li Z., Ye X., Wang S., Wang W., Huang X., (2021). Exploring the vertical dimension of street view image based on deep learning: a case study on lowest floor elevation estimationInternational Journal of Geographical Information Science, 1-26.

Li Z., Huang X., Hu T., Ning H., Ye X., Huang B., Li X., (2021), ODT FLOW: A Scalable Platform for Extracting, Analyzing, and Sharing Multi-source Multi-scale Human MobilityPlos One, 16(8): e0255259.

Xu D., Huang X., Mango J., Li X., Li Z., (2021), Simulating multi-exit evacuation using deep reinforcement learningTransactions in GIS,

Ye X., Wang W., Zhang X., Li Z., Yu D., Du J., Chen Z., (2021), Reconstructing spatial information diffusion networks with heterogeneous agents and text contentsTransactions in GIS,

Li Z., Li X., Porter D., Zhang J., Jiang Y., Olatosi B., Weissman S. (2020)  Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data AnalyticsJMIR Research Protocols,

Ning H., Li Z., Wang C., Yang L. (2020) Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation,  Annals of GIS,

Huang X., Wang C., Li Z., Ning H., Kim H., (2020), A 100m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprintsBig Earth Data,

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;

Ning H., Li Z., Wang C., Yang L. (2020) Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation,  Annals of GIS,

Xu D., Huang X., Li Z., Li X. (2020), Local Motion Simulation using Deep Reinforcement LearningTransactions in GIS,

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;

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)

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,

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),

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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