Sydney Informatics Hub presents:
AgReFed Data-Harvester Workshop for R users
The AgReFed Data-Harvester enables reusable workflows for data downloads, feature extraction and spatial-temporal processing of ecological and environmental data into ready-made datasets for machine learning and geospatial insight.
In this hands-on workshop, we will showcase:
1. Automatic download and extraction of:
- Landsat/Sentinel data via Google Earth Engine (e.g., seasonal NDVI)
- National calibrated Landsat/Sentinel data via Digital Earth Australia (DEA) Geoscience Earth Observations
- Soil and Landscape Grid of Australia (SLGA)
- SILO Climate Database (e.g., temperature, rainfall)
- National Digital Elevation Model (DEM) 1 arcsec
- Radiometric Data
2. Spatial and temporal extraction and processing from obtained datasets
3. Visualisation of data layers and satellite images from Google Earth Engine
4. Cloud-coverage masking for satellite image processing
5. Wrangling points of interest into a machine learning-friendly format
Target audience:
This workshop will be of interest to researchers and students in Agriculture, Environmental Science, Geosciences, as well as others interested in working with geo-spatial datasets, especially via spatio-temporal processing of open environmental and satellite-derived data. Additionally, anyone interested in joining an early-phase open-source project are encouraged to come and help develop!
Workshop format:
3 hours online (with breaks). Registered attendees will receive a Zoom link closer to the date.
Pre-requisites:
A knowledge of R and RStudio and installing packages in R and Python. An interactive online environment will be provided for users for the workshop (with installation instructions provided for those wishing to run things locally).
Note: The AgReFed Data-Harvester is accessible via both R and Python. If you would like to attend a Python workshop instead of (or in addition to) the R stream, please let us know of your interest by entering your email here: https://sydney.au1.qualtrics.com/jfe/form/SV_9TUKiEjaNEFPifk
For more information:
sih.training@sydney.edu.au