Geospatial data providers have adopted a variety of science gateways as the primary method for accessing remote geospatial data. Early systems provided little more than a simple file transfer mechanism but over the past decade, advanced features were incorporated to allow users to retrieve data seamlessly without concern for native file formats, data resolution, or even spatial projections. However, the recent growth in Deep Learning models in the geospatial domains has exposed additional requirements for accessing geospatial repositories. In this paper we discussed the major data accessibility challenges faced by the Deep Learning community namely: (1) reproducibility of data preprocessing workflows, (2) optimizing data transfer between gateways and computational environments, and (3) minimizing local storage requirements using on-the-fly augmentation. In this paper, we present our vision of spatial data generators to act as middleware between geospatial data gateways and Deep Learning models. We propose advanced features for spatial data generators and describe how they could satisfy the data accessibility requirements of the geospatial Deep Learning community. Lastly, we argue that satisfying these data accessibility requirements will not only enhance the reproducibility of Deep Learning workflows and speed their development but will also improve the quality of training and prediction of operational Deep Learning models.

Summary In recent years, geospatial data have exploded to massive volume and diversity and subsequently cause serious usability issues for researchers in various scientific areas. This paper describes a cyberGIS community data service framework to facilitate geospatial big data access, processing, and sharing based on a hybrid supercomputer architecture. Specifically, the framework aims to enhance the usability of national elevation dataset released by the U.S. Geological Survey in the contiguous United States at the resolution of arc‐second. A community data service, namely TopoLens, is created to demonstrate the workflow integration of national elevation dataset and the associated computation and analysis. Two user‐friendly environments, including a publicly available web application and a private workspace based on the Jupyter notebook, are provided for users to access both precomputed and on‐demand computed high‐resolution elevation data. The system architecture of TopoLens is implemented by exploiting the ROGER supercomputer, the first cyberGIS supercomputer dedicated to geospatial problem‐solving. The usability of TopoLens has been acknowledged in the topographic user community evaluation.

Summary The interdisciplinary field of cyberGIS (geographic information science and systems (GIS) based on advanced cyberinfrastructure) has a major focus on data‐ and computation‐intensive geospatial analytics. The rapidly growing needs across many application and science domains for such analytics based on disparate geospatial big data poses significant challenges to conventional GIS approaches. This paper describes CyberGIS‐Jupyter, an innovative cyberGIS framework for achieving data‐intensive, reproducible, and scalable geospatial analytics using Jupyter Notebook based on ROGER, the first cyberGIS supercomputer. The framework adapts the Notebook with built‐in cyberGIS capabilities to accelerate gateway application development and sharing while associated data, analytics, and workflow runtime environments are encapsulated into application packages that can be elastically reproduced through cloud‐computing approaches. As a desirable outcome, data‐intensive and scalable geospatial analytics can be efficiently developed and improved and seamlessly reproduced among multidisciplinary users in a novel cyberGIS science gateway environment.

The exploratorium described here consists of hyper-linked tutorials and quizzes on high performance computational concepts and computational modules implemented on different computer systems: desktop workstations, vector and parallel supercomputers. The exploratorium provides a highly interactive environment in which students can experiment with computational modules and visually compare algorithms, numerical techniques, performance and implementation related issues. Such a hands-on simulation and experimentation based learning will enable students to gain a deeper understanding of advanced concepts. The World Wide Web based exploratorium fosters the easy development of instructional material from research software.>

Article Experiments with a gigabit neuroscience application on the CM-2 Share on Authors: T. T. Kwan National Center for Supercomputing Applications, University of Illinois, Urbana, Illinois National Center for Supercomputing Applications, University of Illinois, Urbana, IllinoisView Profile , J. A. Terstriep National Center for Supercomputing Applications, University of Illinois, Urbana, Illinois National Center for Supercomputing Applications, University of Illinois, Urbana, IllinoisView Profile Authors Info & Claims Supercomputing '93: Proceedings of the 1993 ACM/IEEE conference on SupercomputingDecember 1993 Pages 133–142https://doi.org/10.1145/169627.169671Online:01 December 1993Publication History 2citation122DownloadsMetricsTotal Citations2Total Downloads122Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access