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--- |
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task_categories: |
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- image-segmentation |
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tags: |
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- roboflow |
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- roboflow2huggingface |
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- Aerial |
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- Logistics |
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- Construction |
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- Damage Risk |
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- Other |
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--- |
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<div align="center"> |
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<img width="640" alt="keremberke/satellite-building-segmentation" src="https://huggingface.co/datasets/keremberke/satellite-building-segmentation/resolve/main/thumbnail.jpg"> |
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</div> |
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### Dataset Labels |
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``` |
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['building'] |
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``` |
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### Number of Images |
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```json |
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{'train': 6764, 'valid': 1934, 'test': 967} |
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``` |
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### How to Use |
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- Install [datasets](https://pypi.org/project/datasets/): |
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```bash |
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pip install datasets |
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``` |
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- Load the dataset: |
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```python |
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from datasets import load_dataset |
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ds = load_dataset("keremberke/satellite-building-segmentation", name="full") |
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example = ds['train'][0] |
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``` |
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### Roboflow Dataset Page |
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[https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1](https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1?ref=roboflow2huggingface) |
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### Citation |
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``` |
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@misc{ buildings-instance-segmentation_dataset, |
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title = { Buildings Instance Segmentation Dataset }, |
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type = { Open Source Dataset }, |
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author = { Roboflow Universe Projects }, |
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howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } }, |
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url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation }, |
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journal = { Roboflow Universe }, |
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publisher = { Roboflow }, |
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year = { 2023 }, |
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month = { jan }, |
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note = { visited on 2023-01-18 }, |
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} |
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``` |
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### License |
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CC BY 4.0 |
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### Dataset Summary |
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This dataset was exported via roboflow.com on January 16, 2023 at 9:09 PM GMT |
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Roboflow is an end-to-end computer vision platform that helps you |
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* collaborate with your team on computer vision projects |
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* collect & organize images |
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* understand and search unstructured image data |
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* annotate, and create datasets |
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* export, train, and deploy computer vision models |
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* use active learning to improve your dataset over time |
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For state of the art Computer Vision training notebooks you can use with this dataset, |
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visit https://github.com/roboflow/notebooks |
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To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com |
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The dataset includes 9665 images. |
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Buildings are annotated in COCO format. |
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The following pre-processing was applied to each image: |
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* Auto-orientation of pixel data (with EXIF-orientation stripping) |
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No image augmentation techniques were applied. |
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