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| license: cc-by-4.0 | |
| sdk: streamlit | |
| sdk_version: 1.25.0 | |
| colorFrom: blue | |
| pinned: false | |
| title: Biomap | |
| emoji: 🐢 | |
| colorTo: green | |
| app_file: biomap/streamlit_app.py | |
| # Welcome to the project inno-satellite-images-segmentation-gan | |
|  | |
| - **Project name**: inno-satellite-images-segmentation-gan | |
| - **Library name**: library | |
| - **Authors**: Ekimetrics | |
| - **Description**: Segmenting satellite images in a large scale is challenging because grondtruth labels are spurious for medium resolution images (Sentinel 2). We want to improve our algorithm either with data augmentation from a GAN, or to correct or adjust Corine labels. | |
| ## Project Structure | |
| ``` | |
| - library/ # Your python library | |
| - data/ | |
| - raw/ | |
| - processed/ | |
| - docs/ | |
| - tests/ # Where goes each unitary test in your folder | |
| - scripts/ # Where each automation script will go | |
| - requirements.txt # Where you should put the libraries version used in your library | |
| ``` | |
| ## Branch strategy | |
| TBD | |
| ## Ethics checklist | |
| TBD | |
| ## Starter package | |
| This project has been created using the Ekimetrics Python Starter Package to enforce best coding practices, reusability and industrialization. <br> | |
| If you have any questions please reach out to the inno team and [Théo Alves Da Costa](mailto:theo.alvesdacosta@ekimetrics.com) |