My name is Fei Hu. I’m a software engineer, and interested in the application development for the domains of computer vision, big data, remote sensing, and GIS. I am also actively working on the open-source techniques, such as TensorFlow, Spark, MapReduce, HDFS, and Oozie.


  • Hu, F., Yang, C., Jiang, Y., Li, Y., Song, W., Duffy, D.Q., Schnase, J.L. and Lee, T., 2018. A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data. International Journal of Digital Earth, pp.1-19.

  • Hu, F., Li, Z., Yang, C. and Jiang, Y., 2018. A graph-based approach to detecting tourist movement patterns using social media data. Cartography and Geographic Information Science, pp.1-15.

  • Hu, F., Xu, M., Yang, J., Liang, Y., Cui, K., Little, M.M., Lynnes, C.S., Duffy, D.Q. and Yang, C., 2018. Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data. ISPRS International Journal of Geo-Information, 7(4), p.144.

  • Hu, F., Yang, C., Schnase, J.L., Duffy, D.Q., Xu, M., Bowen, M.K., Lee, T. and Song, W., 2018. ClimateSpark: An in-memory distributed computing framework for big climate data analytics. Computers & Geosciences, 115, pp.154-166.

  • Jiang, Y., Li, Y., Yang, C., Hu, F., Armstrong, E.M., Huang, T., Moroni, D., McGibbney, L.J. and Finch, C.J., 2018. Towards intelligent geospatial data discovery: A machine learning framework for search ranking. International Journal of Digital Earth, 11(9), pp.956-971.

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

  • Yang, C., Huang, Q., Li, Z., Liu, K. and Hu, F., 2017. Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), pp.13-53.

  • Li, Z., Huang, Q., Carbone, G.J. and Hu, F., 2017. A high performance query analytical framework for supporting data-intensive climate studies. Computers, Environment and Urban Systems, 62, pp.210-221.

  • Yang, C., Yu, M., Hu, F., Jiang, Y. and Li, Y., 2017. Utilizing Cloud Computing to address big geospatial data challenges. Computers, Environment and Urban Systems, 61, pp.120-128.

  • Li, Z., Yang, C., Liu, K., Hu, F. and Jin, B., 2016. Automatic Scaling Hadoop in the Cloud for Efficient Process of Big Geospatial Data. ISPRS International Journal of Geo-Information, 5(10), p.173.

  • Li, Y., Jiang, Y., Hu, F., Yang, C., Huang, T., Moroni, D. and Fench, C., 2016, September. Leveraging cloud computing to speedup user access log mining. In OCEANS 2016 MTS/IEEE Monterey (pp. 1-6). IEEE.

  • Hu, F., Bowen, M.K., Li, Z., Schnase, J.L., Duffy, D., Lee, T.J. and Yang, C.P., 2015, December. A Columnar Storage Strategy with Spatiotemporal Index for Big Climate Data. In AGU Fall Meeting Abstracts.

  • Song, W.W., Jin, B.X., Li, S.H., Wei, X.Y., Li, D. and Hu, F., 2015. Building Spatiotemporal Cloud Platform for Supporting GIS Application. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4), p.55.

  • Wang, C., Hu, F., Hu, X., Zhao, S., Wen, W. and Yang, C., 2015. A Hadoop-Based Distributed Framework for Efficient Managing and Processing Big Remote Sensing Images. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4), p.63.

  • Wang, Z., Yao, Z., Gu, G., Hu, F. and Dai, X., 2014. _Multi‐agent‐based simulation on technology innovation‐diffusion in China_. Papers in Regional Science, 93(2), pp.385-408.