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File: 'Arosioetal2023_FCNN_mod_map.zip'
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Morphological map of the Irish continental shelf created using Deep Learning [dasid=8521] show more
File properties
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
Metadata
Content
Type of data
Output data
Data origin
derived/modelled/interpolated
Materials and methods
Model Name
Fully Convolutional Neural Networks
Version
State-variables
Temporal Scope
First date
Last date
Geographic Scope
Sea area(s)
Location
Additional information
Other info
Link file
| Morphological map of the Irish continental shelf created using Deep Learning [dasid=8521] show more |
File properties
| 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 |
Metadata
| Content | |
| Type of data | Output data |
|---|---|
| Data origin | derived/modelled/interpolated |
| Materials and methods | |
| Model Name | Fully Convolutional Neural Networks |
| Version | |
| State-variables | |
| Temporal Scope | |
| First date | |
| Last date | |
| Geographic Scope | |
| Sea area(s) | |
| Location | |
| Additional information | |
| Other info | |
| Link file |