Continental-scale predicted ecosystem condition for Australia using deep learning

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.