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.
Publication
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.