Urbana, IL 61801
- Ecological economics
- Sustainability science
- High performance geospatial computing
My research interests are in the general area of sustainability science, GeoAI, and big data. I am interested in developing multi-source data model to assess socioeconomic development in less developed regions. I am also interested in developing deep learning and machine learning algorithmic optimizations and utilizing cutting-edge computing technologies for efficient geospatial big data analytics.
- PhD - University of Denver
- MS - University of Pennsylvania
- BS - University of California, Berkeley
- High Performance Geospatial Computing
- Spatial Problem Solving
- GeoAI and Machine Learning
- Wang, X*., Rafa, M., Moyer, J. D., Li, J., Scheer, J., & Sutton, P. (2019). Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery. Remote Sensing, 11(2), 163.
- Li, J*., Wang, X., Zhang, T., & Xu, Y. (2018). Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach. ISPRS International Journal of Geo-Information, 7(7), 239.
- Wang, X., Li, J*., & Zhang, T. (2019). A Machine-Learning Model for Zonal Ship Flow Prediction Using AIS Data: A Case Study in the South Atlantic States Region. Journal of Marine Science and Engineering, 7(12), 463.
- Wang, X*., Sutton, P. C., & Qi, B. (2019). Global Mapping of GDP at 1 km2 Using VIIRS Nighttime Satellite Imagery. ISPRS International Journal of Geo-Information, 8(12), 580.
- Wang X*., Li J., Zhang T., 2019. Building a GPU-enabled analytical workflow for maritime pattern discovery using Automatic Identification System data. In: Tang W. Wang S. eds, High Performance Computing for Geospatial Applications. Springer.