| Filename | Arosioetal2023_FCNN_mod_map.zip |
| Direct link | https://mda.vliz.be/directlink.php?fid=VLIZ_00001115_65f455c54bd37443176027 |
| Datatype | Model Data |
| MIMEtype | application/x-zip-compressed |
| Authors | Arosio, R.; Hobley, B.; Wheeler, A.; Sacchetti, F.; Conti, L.; Furey, T.; Lim, A. |
| Dataprovider | Arosio Riccardo |
| Email Dataprovider | rarosio@ucc.ie |
| Conditions of use | CC-BY 4.0 |
| Creationdate | |
| Submitter | Marquez Laura |
| Submit date | 2024-03-15 14:05:57 |
| Archived by | Marquez Laura |
| Archive date | 2024-03-15 14:24:56 |
| Path | Mission_Atlantic/Map_Irish_continental_shelf_using_Deep_Learning/ |
| Start year | |
| End year | |
| Summary | Morphological map (10 classes) of the Irish shelf resulting from the modal aggregation of the qualitatively and quantitatively best Fully Convolutional Neural Networks models obtained in the study from Arosio et al. 2023. |
| Description | Morphological map (10 classes) of the Irish shelf resulting from the modal aggregation (Cell statistics “MAJORITY” in ArcGIS Pro 3.1) of the qualitatively and quantitatively best Fully Convolutional Neural Networks models obtained in the study: Arosio, R., Hobley, B., Wheeler, A. J., Sacchetti, F., Conti, L. A., Furey, T. and A. Lim, 2023. Fully convolutional neural networks applied to large-scale marine morphology mapping. Frontiers in Marine Science, Sec. Ocean Observation, 10, https://doi.org/10.3389/fmars.2023.1228867 |
| Changes | |
