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YAML Metadata Warning: The task_categories "change-detection" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

OSCD RGB

OSCD RGB

The Onera Satellite Change Detection (OSCD) dataset is a change detection dataset that focuses on multispectral (MS) images taken by the ESA Sentinel-2 satellite. This dataset provides 24 registered image pairs from various continents, captured between the years 2015 and 2018. Along with the image pairs, the dataset includes binary change masks that highlight areas where changes have occurred. Pixel-level change ground truth is provided for all 14 training and 10 test image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms.

Description

The dataset is available in two versions: RGB only (this repo) and all 13 Multispectral (MS) Sentinel-2 bands.

  • Total Number of Images: 24
  • Bands: 3 (RGB)
  • Image Size: 10000x10000
  • Image Resolution: 10m
  • Land Cover Classes: 2
  • Classes: no change, change
  • Source: Sentinel-2

Usage

To use this dataset, simply use datasets.load_dataset("blanchon/OSCD_RGB").

from datasets import load_dataset
OSCD_RGB = load_dataset("blanchon/OSCD_RGB")

Citation

If you use the EuroSAT dataset in your research, please consider citing the following publication:

@article{daudt2018urban,
  title     = {Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks},
  author    = {R. C. Daudt and B. L. Saux and Alexandre Boulch and Y. Gousseau},
  journal   = {IEEE International Geoscience and Remote Sensing Symposium},
  year      = {2018},
  doi       = {10.1109/IGARSS.2018.8518015},
  bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/18755de13d2d868255bbf332fa80ec218163c74e}
}
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