Datasets:
Tasks:
Image Classification
Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
remote-sensing
earth-observation
geospatial
satellite-imagery
land-cover-classification
USGS National Map
License:
🤗 Add DatasetCard
Browse files
README.md
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---
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language: en
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license: unknown
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task_categories:
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- image-classification
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paperswithcode_id: uc-merced-land-use-dataset
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pretty_name: UC Merced
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tags:
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- remote-sensing
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- earth-observation
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- geospatial
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- satellite-imagery
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- land-cover-classification
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- USGS National Map
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- USGS
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---
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# UC Merced
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<!-- Dataset thumbnail -->
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![UC Merced](./thumbnail.png)
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<!-- Provide a quick summary of the dataset. -->
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The UC Merced Land Use dataset is a land use classification dataset of 2.1k 256x256 1ft resolution RGB images of urban locations around the U.S. extracted from the USGS National Map Urban Area Imagery collection with 21 land use classes (100 images per class).
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- **Paper:** https://arxiv.org/abs/1911.06721
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- **Homepage:** http://weegee.vision.ucmerced.edu/datasets/landuse.html
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## Description
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<!-- Provide a longer summary of what this dataset is. -->
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- **Total Number of Images**: 2100
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- **Bands**: 3 (RGB)
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- **Image Size**: 256x256
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- **Resolution**: 0.3m
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- **Land Cover Classes**: 21
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- **Classes**: agricultural, airplane, baseballdiamond, beach, buildings, chaparral, denseresidential, forest, freeway, golfcourse, harbor, intersection, mediumresidential, mobilehomepark, overpass, parkinglot, river, runway, sparseresidential, storagetanks, tenniscourt
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- **Source**: USGS
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## Usage
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To use this dataset, simply use `datasets.load_dataset("blanchon/UC_Merced")`.
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<!-- Provide any additional information on how to use this dataset. -->
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```python
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from datasets import load_dataset
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UC_Merced = load_dataset("blanchon/UC_Merced")
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```
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## Citation
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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If you use the EuroSAT dataset in your research, please consider citing the following publication:
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```bibtex
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@article{neumann2019indomain,
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title = {In-domain representation learning for remote sensing},
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author = {Maxim Neumann and Andre Susano Pinto and Xiaohua Zhai and Neil Houlsby},
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year = {2019},
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journal = {arXiv preprint arXiv: 1911.06721}
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}
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```
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