Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for DeepGlobe/7-class segmentation of RGB 512x512 high-res. images These Residual-UNet model data are based on the [DeepGlobe dataset](https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset) Models have been created using Segmentation Gym* using the following dataset**: https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset Image size used by model:  512 x 512 x 3 pixels classes: 1. urban 2. agricultural 3. rangeland 4. forest 5. water 6. bare 7. unknown File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D. and Raskar, R., 2018. Deepglobe 2018: A challenge to parse the earth through satellite images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 172-181).

opencc-by-4.0Jan 2023View details →

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images These Residual-UNet model data are based on Chesapeake Land Cover images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://lila.science/datasets/chesapeakelandcover Image size used by model: 512 x 512 x 3 pixels classes: water tree_canopy_forest low_vegetation_field barren land impervious_other impervious_road no_data File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019)

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for EnviroAtlas/6-class segmentation of RGB 512x512 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for EnviroAtlas/6-class segmentation of RGB 512x512 high-res. images These Residual-UNet model data are based on [EnviroAtlas](https://www.mdpi.com/2072-4292/12/12/1909) images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://zenodo.org/record/6268150#.Y91Q-BzMLRa Image size used by model: 512 x 512 x 3 pixels classes: nodata water impervious barren trees herbaceous shrubland File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Pilant, A., Endres, K., Rosenbaum, D. and Gundersen, G., 2020. US EPA EnviroAtlas meter-scale urban land cover (MULC): 1-m pixel land cover class definitions and guidance. Remote sensing, 12(12), p.1909.

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes: other building File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of flooded buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes: other flooded building File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.

Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/damaged buildings in RGB 768x768 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/damaged buildings in RGB 768x768 high-res. images Models have been created using Segmentation Gym* using the following dataset**: https://arxiv.org/abs/1911.09296 These SegFormer model data are based on 1m spatial footprint images and associated labels of undamaged/damaged buildings. Image size used by model: 768 x 768 x 3 pixels classes: no-damage minor-damage major-damage unclassified File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston. Creating xbd: A dataset for assessing building damage from satellite imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019. https://arxiv.org/abs/1911.09296

Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/buildings in RGB 768x768 high-res. images Models have been created using Segmentation Gym* using the following dataset**: https://arxiv.org/abs/1911.09296 These SegFormer model data are based on 1m spatial footprint images and associated labels of buildings. Image size used by model: 768 x 768 x 3 pixels classes: other building File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston. Creating xbd: A dataset for assessing building damage from satellite imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019. https://arxiv.org/abs/1911.09296

Doodleverse/Segmentation Zoo Res-UNet models for Aerial/NOAA ERI/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

Doodleverse/Segmentation Zoo Res-UNet models for Aerial/NOAA ERI/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images Residual-UNet models are trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here**: https://doi.org/10.5281/zenodo.7268082 Models have been created using Segmentation Gym*: Code - https://github.com/Doodleverse/segmentation_gym Paper - https://doi.org/10.1029/2022EA002332 The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 2, corresponding to 2 classes: 1. water  2. other   Included here are 6 files with the same root name:  '.json' config file: this is the file that was used by Segmentation Gym to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. '.h5' weights file: this is the file that was created by the Segmentation Gym function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym function `seg_images_in_folder.py`.  '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`  '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` '.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py` '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Goldstein, Evan B., Buscombe, Daniel, Budavi, Priyanka, Favela, Jaycee, Fitzpatrick, Sharon, Gabbula, Sai Ram Ajay Krishna, Ku, Venus, Lazarus, Eli D., McCune, Ryan, Shah, Manish, Sigdel, Rajesh, & Tagner, Steven. (2022). Segmentation Labels for Emergency Response Imagery from Hurricane Barry, Delta, Dorian, Florence, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7268083

Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Segformer models for Chesapeake/7-class segmentation of RGB 512x512 high-res. images These Segformer model data are based on Chesapeake Land Cover images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://lila.science/datasets/chesapeakelandcover Image size used by model: 512 x 512 x 3 pixels classes: water tree_canopy_forest low_vegetation_field barren land impervious_other impervious_road no_data File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Robinson C, Hou L, Malkin K, Soobitsky R, Czawlytko J, Dilkina B, Jojic N. Large Scale High-Resolution Land Cover Mapping with Multi-Resolution Data. Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019)

Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

Doodleverse/Segmentation Zoo Res-UNet models for Aerial/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images These Residual-UNet models have been created using Segmentation Gym* Image size used by model: 1024 x 768 x 3 pixels classes: <ol> <li>water</li> <li>other</li> </ol> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

Doodleverse/Segmentation Zoo Res-UNet models for Aerial/nadir/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images

Doodleverse/Segmentation Zoo Res-UNet models for Aerial/nadir/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images These Residual-UNet models have been created using Segmentation Gym* using the following dataset**: Image size used by model: 1024 x 768 x 3 pixels classes: <ol> <li>water</li> <li>other</li> </ol> File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **

opencc-by-4.0Nov 2022View details →

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts.

Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 3-band (RGB) images of coasts. Version 3: Updated 2023-04-25 These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384242">https://doi.org/10.5281/zenodo.7384242</a> Classes: {0=other, 1=water} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, D. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384242">https://doi.org/10.5281/zenodo.7384242</a>

opencc-by-4.0May 2023View details →

Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery

Doodleverse/Segmentation Gym Res-UNet models for 2-class (water, other) segmentation of CoastCam runup timestack imagery This model release is part of the Doodleverse: https://github.com/Doodleverse These Residual-UNet model data are based on RGB (red, green, and blue) images of coasts and associated labels. Models have been created using Segmentation Gym* using an as-yet unpublished dataset of images and associated label images. See https://github.com/Doodleverse for more information about how this model was trained, and how to use it for inference Classes: {0=other, 1=water} File descriptions There are two models; v7 has been trained from scratch, and v8 has been fine-tuned using hyperparameter adjustment. For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally,  1. BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU 2. sample_images.zip contains a few example input files, for model testing References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

opencc-by-4.0Jun 2023View details →

Measured data and calculations of pilot RES system for a 103-m2 building in Athens, Greece

This dataset contains complete measurements of an energy system for a building in Athens, Greece (temperatures, flow rates, power, solar radiation, etc.). This system includes a vapour compression heat pump, 4 PVT collectors, a virtual BTES (emulated via a tank with controllable temperature) and three water tanks. A winter and summer day are included. The system operated for space cooling and hot water during the summer day and for space heating and hot water during the winter day. An in-house Python code of NCSR Demokritos has been applied to simulate the energy system operation during these two days for validation purposes. The calculated results are also given in this dataset.

opencc-by-4.0Nov 2020View details →

Supporting data and code for "Brown AW, Bohan Brown MM, Onken KL, Beitz DC. Short-term consumption of sucralose, a nonnutritive sweetener, is similar to water with regard to select markers of hunger signaling and short-term glucose homeostasis in women. Nutr Res. 2011 Dec;31(12):882-8. doi: 10.1016/j.nutres.2011.10.004. PMID: 22153513."

Data and code to support the publication, Brown AW, Bohan Brown MM, Onken KL, Beitz DC. Short-term consumption of sucralose, a nonnutritive sweetener, is similar to water with regard to select markers of hunger signaling and short-term glucose homeostasis in women. Nutr Res. 2011 Dec;31(12):882-8. doi: 10.1016/j.nutres.2011.10.004. PMID: 22153513. <ul> <li>Code was updated 2020 NOV 24 to add comments, but otherwise remains unchanged from 2011.</li> <li>Data file was updated to include a data dictionary, but otherwise remains unchanged from 2011.</li> </ul> Treatment identifiers for the four-arm crossover are clarified in the SAS code. Other details are available in the published article.

opencc-by-4.0Nov 2015View details →

Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004

Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector

opencc-by-4.0Mar 2024View details →

Wind and SOLAR RES predicted production data for Crete and Peloponnese - ONENET WP8

WP8 aimed at the development and implementation of a web based app that enhances Active Power Management necessary for coordination of a TSOs and DSOs, using AI methods and cloud calculation engines that was tested in Peloponnese and Crete regions. Full description of the scope and results of WP8 Greek demo can be found in the relative deliverable D8.2: Development and implementation of the “F-Channel” platform (https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D8.2_V1.0.pdf). For purpose of this project similar, historical weather data in 1 hour resolution have been used in order to obtain behavior patterns of climatic parameters (daily, monthly, season) throughout region of interest. For this purpose various ERA5 climatic datasets has been used and AI algorithms applied in combination with terrain orography data. Modeled results was compared with operational data from TSO/DSO and appropriate model calibration has been provided based on deep learning AI algorithms. The data for Wind power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/wind_res_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">wind_res_onenet_wp8.csv</a>> in the following columns <time> ; <aa> ; <pw> ; <ws> ; <wp_name> . Columns are related to: Hourly time, wind park code, power [MWh], wind speed [m/s] and wind park name, respectively. The data for Solar power plant modelled production is given in file <<a href="../api/records/10848817/draft/files/solar_onenet_wp8.csv/content" target="_blank" rel="noopener noreferrer">solar_onenet_wp8.csv</a>> in the following columns <time> ; <name> ; <pw> ; <ta> ; <ghi> . Columns are related to: Hourly time, solar park name, power in MWh, ambient temperature and Global Horizontal irradiance [W/m2], respectively.

opencc-by-4.0Dec 2021View details →

Supporting Data for Drift Phase Structure Implications for Radiation Belt Transport by T.P. O'Brien et al. submitted to J. Geophysical Res.

Datasets used in Drift Phase Structure Implications for Radiation Belt Transport by T.P. O'Brien et al. submitted to J. Geophysical Res.

opencc-by-4.0Jan 2022View details →

SWMF/CIMI outputs used in Holappa and Buzulukova (2022) manuscript submitted to Geophys. Res. Lett.

These data files and Python scripts are used to produce Figures 1 and 2 in Holappa and Buzulukova, Explicit IMF By-dependence of energetic protons and the ring current, submitted to Geophys. Res. Lett. (2022). PARAM.in.PosTilt and gitinfo.txt contain the settings and Git version references used to setup the SWMF model. Figure 1: PosBy_CimiFlux_n00000046_h.fls, NegBy_CimiFlux_n00000046_h.fls are CIMI output files containing proton fluxes for different energies and pitch angles. These data can be read with CIMIreader.py. Figures in the manuscript can be produced by Run_CIMI_reader.py. Files PosBy_CimiFlux_n00000046_e.fls, NegBy_CimiFlux_n00000046_e.fls can be used to produce corresponding figures for electrons. Figure 2: Data files CIMI_log_NegBy_PosTilt.dat and  CIMI_log_PosBy_PosTilt.dat are CIMI output files containing the ring current energy and other variables. Figures can be produced by Plot_CIMI_Dst.py.