Here one can find the installation, metadata and some manuals on how to use the FineET modules, either stand-alone or together.
Setting up some of these modules may require technical knowledge to for any doubts or questions, please Contact Us or Raise an issue on our Github page.
Coming soon, stay tuned!
- Downloading open-source FineET project
- Installing the software
- Running the models locally
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
 Kevin Bruhwiler*, Paahuni Khandelwal, Daniel Rammer*, Samuel Armstrong*, Sangmi Lee Pallickara, and Shrideep Pallickara. Lightweight, Embeddings Based Storage and Model Construction Over Satellite Data Collections. Proceedings of the IEEE International Conference on Big Data (IEEE BigData). Atlanta, USA. 2020.
 Paahuni Khandelwal, Daniel Rammer, Shrideep Pallickara, and Sangmi Lee Pallickara. Mind the Gap: Generating Imputations for Satellite Data Collections at Myriad Spatiotemporal Scopes. (To appear) Proceedings of the 21st IEEE/ACM international Symposium on Cluster, Cloud and Internet Computing (CCGrid). 2021. Melbourne, Australia.
 Rammer, Daniel, Kevin Bruhwiler, Paahuni Khandelwal, Samuel Armstrong, Shrideep Pallickara, and Sangmi Lee Pallickara. “Small is Beautiful: Distributed Orchestration of Spatial Deep Learning Workloads.” In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), pp. 101-111. IEEE Computer Society, 2020.
 Rammer, Daniel, Sangmi Lee Pallickara, and Shrideep Pallickara. “Towards Timely, Resource-Efficient Analyses Through Spatially-Aware Constructs within Spark.” In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC), pp. 46-56. IEEE, 2020.
 Bruhwiler, Kevin, Thilina Buddhika, Shrideep Pallickara, and Sangmi Lee Pallickara. “Iris: Amortized, Resource Efficient Visualizations of Voluminous Spatiotemporal Datasets.” In 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), pp. 47-56. IEEE, 2020.