From CSIRO DAP
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Continental-scale predicted ecosystem condition for Australia using deep learning

Created 20/09/2025

Updated 20/09/2025

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These spatial datasets represent a continental-scale approach to ecosystem condition monitoring using Earth observations and deep learning. This was undertaken for the years 2010, 2015, 2020, 2021 and 2022 for Australia at 100 m resolution. For each predicted ecosystem condition dataset (e.g. predicted_ecosystem_condition_2022.tif), a condition score of 1 indicates at or near reference condition, where a condition score of 0 indicates a fully degraded condition. Accompanying the predicted ecosystem condition datasets are QGIS colour schema (.qml). We also include datasets on spatial and temporal model sensitivity, including mean absoluate error (MAE) of predicted ecosystem condition by bioregions, as well as standard deviation of each pixels predicted ecosystem condition value over time, for all years mapped. These datasets are associated with a manuscript currently undergoing peer-review.

Files and APIs

Additional Info

Field Value
Title Continental-scale predicted ecosystem condition for Australia using deep learning
Language English
Licence Not Specified
Landing Page https://data.gov.au/data/dataset/ac1c115d-bb4c-5987-86fb-d2076102efdc
Contact Point
CSIRO Data Access Portal
CSIROEnquiries@csiro.au
Reference Period 01/01/2000
Geospatial Coverage Australia
Data Portal CSIRO DAP

Data Source

This dataset was originally found on CSIRO DAP "Continental-scale predicted ecosystem condition for Australia using deep learning". Please visit the source to access the original metadata of the dataset:
https://data.csiro.au/collection/csiro:65931