README.md

metadata

task_categories:
  - image-segmentation
tags:
  - roboflow
  - roboflow2huggingface
  - Aerial
  - Logistics
  - Construction
  - Damage Risk
  - Other

Dataset Labels

['building']

Number of Images

{'train': 6764, 'valid': 1934, 'test': 967}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("keremberke/satellite-building-segmentation", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1

Citation

@misc{ buildings-instance-segmentation_dataset,
    title = { Buildings Instance Segmentation Dataset },
    type = { Open Source Dataset },
    author = { Roboflow Universe Projects },
    howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } },
    url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jan },
    note = { visited on 2023-01-18 },
}

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on January 16, 2023 at 9:09 PM GMT

Roboflow is an end-to-end computer vision platform that helps you

• collaborate with your team on computer vision projects
• collect & organize images
• understand and search unstructured image data
• annotate, and create datasets
• export, train, and deploy computer vision models
• use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 9665 images. Buildings are annotated in COCO format.

The following pre-processing was applied to each image:

• Auto-orientation of pixel data (with EXIF-orientation stripping)

No image augmentation techniques were applied.