ac1c115d-bb4c-5987-86fb-d2076102efdc urn:uuid en UTF-8 nonGeographicDataset nonGeographicDataset 2025-09-20T11:50:31 creation ISO 19115 2016 Continental-scale predicted ecosystem condition for Australia using deep learning ac1c115d-bb4c-5987-86fb-d2076102efdc 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. predicted_ecosystem_condition_2010.qml documentDigital HTML predicted_ecosystem_condition_2010.tif documentDigital TIFF predicted_ecosystem_condition_2015.qml documentDigital HTML predicted_ecosystem_condition_2015.tif documentDigital TIFF predicted_ecosystem_condition_2020.qml documentDigital HTML predicted_ecosystem_condition_2020.tif documentDigital TIFF predicted_ecosystem_condition_2021.qml documentDigital HTML predicted_ecosystem_condition_2021.tif documentDigital TIFF predicted_ecosystem_condition_2022.qml documentDigital HTML predicted_ecosystem_condition_2022.tif documentDigital TIFF predicted_ecosystem_condition_MAE_bioregions.gpkg documentDigital HTML predicted_ecosystem_condition_MAE_bioregions.qml documentDigital HTML predicted_ecosystem_condition_stddev.qml documentDigital HTML predicted_ecosystem_condition_stddev.tif documentDigital TIFF