SOMOSPIE is a modular SOil MOisture SPatial Inference Engine that allows Earth scientists to address
the coarse-grained resolution and spatial information gaps associated with satellite data. The modular components of SOMOSPIE consists of:
Input of available satellite data at its native spatial
resolution.
Selection of a geographic region of interest.
Prediction, of missing values across the entire region of
interest (i.e., gap-filling) and at finer-grained resolution.
Analysis and visualization of generated predictions.
SOMOSPIE and FAIR
SOMOSPIE supports reproducibility, explainability, and portability of results. Its new features allow
users to:
Deploy container technology on cloud platforms to perform rapid
data movement and achieve portability.
Collect workflow execution's record trails to enable data
traceability and results explainability.
GEOtiled is a modular workflow that allows the rapid computation of terrain parameters (e.g., slope, aspect, hillshade)
from Digital Elevation Models (DEMs) by leveraging data decomposition and parallel processing. GEOtiled allows:
Download available DEM data from the United States Geological Survey (USGS) webpage.
Over 15 different computable terrain parameters.
Generation of terrain parameters using either the GDAL or SAGA library.
Visualization of generated results.
GEOtiled and FAIR
GEOtiled supports reproducibility, explainability, and portability of results. Its new features allow users to:
Index their data in easily searchable repositories.
Access public platforms such as GeoTIFF and Shapefile.
Operate common geospatial formats for easy use on other software or systems.
Document data curation along with the organization and content of files.
Gabriel Laboy, Ian Lumsden, Jack Marquez, Kin Wai NG Lugo, Rodrigo Vargas, and Michela Taufer.
A Modular, Cross-Platform Toolkit for High-Resolution Terrain Parameter Analysis.In Proceedings of the 21st IEEE International Conference on eScience (eScience), Chicago, IL, USA, September 2025. IEEE Computer Society. (Acceptance Rate: 33/98, 33.6%).
Gabriel Laboy, Paula Olaya, Jack Marquez, Michael Sutherlin, Rodrigo Vargas, and Michela Taufer.
Advancing the GEOtiled Framework Through Scalable Terrain Parameter Computation. In Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing (HPDC), pages 1–2, Notre Dame, IN, USA, July 20–23 2025. ACM. (Short Paper).
Befikir Bogale, Ian Lumsden, Dalal Sukkari, Dewi Yokelson, Stephanie Brink, Olga Pearce, and Michela Taufer.
Surrogate Models for Analyzing Performance Behavior of HPC Applications Using RAJAPerf.In Proceedings of the International Conference on Computational Science (ICCS), page 1–8, Singapore, July 7–9 2025. Springer.[link]
@InProceedings{10.1007/978-3-031-97635-3_39,
author="Bogale, Befikir and Lumsden, Ian and Sukkari, Dalal and Yokelson, Dewi and Brink, Stephanie and Pearce, Olga and Taufer, Michela",
editor="Lees, Michael H. and Cai, Wentong and Cheong, Siew Ann and Su, Yi and Abramson, David and Dongarra, Jack J. and Sloot, Peter M. A.",
title="Surrogate Models for Analyzing Performance Behavior of HPC Applications Using the RAJA Performance Suite",
booktitle="Computational Science -- ICCS 2025",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="327--335",
isbn="978-3-031-97635-3"
}
Paula Olaya, Sophia Wen, Jay Lofstead, and Michela Taufer.
PerSSD: Persistent, Shared, and Scalable Data with Node-Local Storage for Scientific Workflows in Cloud Infrastructure.In Proceedings of the 2024 IEEE International Conference on Big Data, Washington DC, US, December 2024.
IEEE Computer Society. (Acceptance Rate: 600/124, 18.8%).
[link]
@INPROCEEDINGS{10826021,
author={Olaya, Paula and Wen, Sophia and Lofstead, Jay and Taufer, Michela},
booktitle={2024 IEEE International Conference on Big Data (BigData)},
title={PerSSD: Persistent, Shared, and Scalable Data with Node-Local Storage for Scientific Workflows in Cloud Infrastructure},
year={2024},
volume={},
number={},
pages={272-281},
keywords={Automation;Software architecture;File systems;Pipelines;Information sharing;Geoscience;Manuals;Performance gain;Reproducibility of results;Noise measurement},
doi={10.1109/BigData62323.2024.10826021}}