| --- |
| dataset_info: |
| features: |
| - name: rgb |
| dtype: image |
| - name: mask |
| dtype: image |
| - name: split |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 3092369713 |
| num_examples: 213758 |
| download_size: 3096602246 |
| dataset_size: 3092369713 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - 100K<n<1M |
| --- |
| # Sen2 Lulc |
|
|
| The Sen2_LULC dataset provides real multispectral satellite imagery for semantic segmentation of land use and land cover in the central Indian region. Captured by Sentinel-2 at 10-meter resolution during February-March 2021, the data was collected from a satellite platform over field environments, offering high-quality inputs for agricultural and environmental monitoring applications. The dataset contains 213,758 images with pixel-level mask annotations. |
| |
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
| |
| The original train/test/val split has been preserved in the `split` column. |
| |
| ## Citation |
| |
| ```bibtex |
| @article{sawant2023sen, |
| title={Sen-2 LULC: Land use land cover dataset for deep learning approaches}, |
| author={Sawant, Suraj and Garg, Rahul Dev and Meshram, Vishal and Mistry, Shrayank}, |
| journal={Data in Brief}, |
| volume={51}, |
| pages={109724}, |
| year={2023}, |
| publisher={Elsevier} |
| } |
| ``` |
| |
| Sawant, Suraj; Garg, Rahul Dev; Meshram, Vishal; Mistry, Shrayank (2023), “Sen-2 LULC ”, Mendeley Data, V3, doi: 10.17632/f4ky6ks248.3 |
| |
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
