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Geoweaver is a web system allowing users to easily compose and execute full-stack deep learning workflows by taking advantage of online spatial data facilities, high-performance computation platforms, and open-source deep learning libraries. It is a perfect alternative to SSH client (e.g., Putty), FTP client, and scientific workflow software.

Through the ESIP Lab project, the team succeeded in:

  • turning large-scale distributed deep network into manageable modernized workflows;
  • boosting higher utilization ratio of the existing cyberinfrastructures by separating scientists from tedious technical details;
  • enhancing the frequency and accuracy of classified land cover land use maps for agricultural purposes;
  • enabling the tracking of provenance by recording the execution logs in structured tables to evaluate the quality of the result maps;
  • prove the effectiveness of operationally using large-scale distributed deep learning models in classifying Landsat image time series


  • Ziheng Sun, et al, Crop Mapping by Deep Neural Network and Cyberinfrastructure, ISPRS Journal of Photogrammetry and Remote Sensing, under review.
  • Ziheng Sun, et al, Advanced Cyberinfrastructure for Intercomparison and Validation of Climate Models, Environmental Modelling and Software, under review.
  • Ziheng Sun, et al, Advanced Geospatial Cyberinfrastructure for Pulling GIScience into AI Phase, in preparation.
  • “Aha” Moments Abound at ESIP’s Custom Bootcamp


  • Presentations and posters on Figshare.


  • 2018 InnoCentive Challenge Winner