Image Segmentation
fastai
PyTorch
geospatial
remote-sensing
semantic-segmentation
sentinel-1
alphaearth
surface-water
Instructions to use rohitm9/surfaceWaterGlobal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- fastai
How to use rohitm9/surfaceWaterGlobal with fastai:
from huggingface_hub import from_pretrained_fastai learn = from_pretrained_fastai("rohitm9/surfaceWaterGlobal") - Notebooks
- Google Colab
- Kaggle
Download evaluation/planetscope_53_scenes_main_table.csv from rohitm9/surfaceWaterGlobal: direct link, hf CLI and curl.
- Browser
- Download file 490 Bytes
-
https://huggingface.co/rohitm9/surfaceWaterGlobal/resolve/main/evaluation/planetscope_53_scenes_main_table.csv
- Command line
-
hf download hf://rohitm9/surfaceWaterGlobal/evaluation/planetscope_53_scenes_main_table.csv
-
curl -L -o planetscope_53_scenes_main_table.csv https://huggingface.co/rohitm9/surfaceWaterGlobal/resolve/main/evaluation/planetscope_53_scenes_main_table.csv
490 Bytes
| method,input,threshold,precision,recall,dice,pooled_water_iou,per_scene_mean_water_iou,ci95_low,ci95_high | |
| S1 only,S1,0.3,0.933377,0.581648,0.716683,0.558462,0.443426,0.354555,0.532611 | |
| S1+AEF (t-1),S1 + previous-year AEF,0.3,0.882159,0.94585,0.912895,0.839748,0.740894,0.672329,0.803297 | |
| S1+AEF (t),S1 + acquisition-year AEF,0.3,0.892125,0.948362,0.919385,0.850797,0.755753,0.688743,0.816403 | |
| OPERA DSWx-S1,S1 binary 30 m,0.3,0.857628,0.85159,0.854598,0.746113,0.590528,0.508065,0.669592 | |