By: Pat Clark and Rowan Gaffney
Use Cases
Machine Learning
- Classification
- Clustering
- Regression
Modeling
- Process Based or Statistical Models
Time Series Analysis
- Estimating Productivity
- Land Use / Land Change
Geostatistics
- Spatial Variance or Autocorrelation
- Kriging / Interpolation
Processing Data
- UAS DN/Radiance to Reflectance
When to Use SCINet?
Setting up analyses to run on SCINet involves a non-trivial amount of overhead. Therefore, you should first evaluate if SCINet is an appropriate avenue for your research. Typically, analyses that are well-suited for SCINet are:
- CPU intensive workloads
- high memory workloads
Additional considerations are:
- Are my analyses already optimized?
- Will I need to parallelize my analyses (typical for CPU intensive workloads)?
- Will I require more than a single node of compute power (ie. distributed computing)?
Tools and Software
The following tools/software are currently available on SCINet. (See the Preinstalled Software List for a full list of currently available software.)
Geospatial Specific Software
- ENVI (5.5): Image analysis software (1 license available)
- ESMF: Earth System Modeling Framework (7.1): High-performance, flexible software infrastructure for building and coupling weather, climate, and related Earth science applications
- Proj4 (4.9.3): Generic coordinate transformation software
- GDAL/OGR: Geospatial Data Abstraction Library (1.11.4): Library for reading and writing raster and vector geospatial data formats
Applicable General Software
- H2O (3.2.0.3): Distributed in-memory machine learning platform with APIs in R and Python
- Python (3.6.6): Interpreted, high-level, general-purpose programming language
- R (3.5.2): Software environment for statistical computing and graphics
- RStudio and RStudio Server: An integrated development environment (IDE) for R
- JupyterLab: Web-based user interface for Project Jupyter
Other
- SCINet Remote Sensing Container Image: Python+R geospatial libraries and JupyterLab IDE (R, IDL, and Python kernels).