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Meta_Album_RSD_Extended

Meta_Album_RSD_Extended

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## Meta-Album RSD Dataset (Extended) * RSD46 dataset (https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU) is collected from Google Earth and Tianditu. The collection contains 46 scene categories, with a total of 117 000 images. Each scene category has between 500 - 3000 images. The original resolution are 256x256 px or 512x512 px. We have created preprocessed version of RSD for Meta-Album by resizing the original dataset to 128x128 px. ### Dataset Details ![](https://meta-album.github.io/assets/img/samples/RSD.png) Meta Album ID: REM_SEN.RSD Meta Album URL: [https://meta-album.github.io/datasets/RSD.html](https://meta-album.github.io/datasets/RSD.html) Domain ID: REM_SEN Domain Name: Remote Sensing Dataset ID: RSD Dataset Name: RSD Short Description: Remote sensing dataset \# Classes: 43 \# Images: 43825 Keywords: remote sensing, satellite image, aerial image, land cover Data Format: images Image size: 128x128 License (original data release): Open for research and non-profit purposes License (Meta-Album data release): CC BY-NC 4.0 License URL (Meta-Album data release): [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) Source: RSD46-WHU Source URL: https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU Original Author: Yang Long, Yiping Gong, Zhifeng Xiao, and Qing Liu, Deren Li, Chunshan Wei, Gefu Tang and Junyi Liu Original contact: longyang@whu.edu.cn Meta Album author: Phan Anh VU Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: meta-album@chalearn.org Contact URL: [https://meta-album.github.io/](https://meta-album.github.io/) ### Cite this dataset ``` @ARTICLE{7827088, author={Long, Yang and Gong, Yiping and Xiao, Zhifeng and Liu, Qing}, journal={IEEE Transactions on Geoscience and Remote Sensing}, title={Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks}, year={2017}, volume={55}, number={5}, pages={2486-2498}, doi={10.1109/TGRS.2016.2645610}} @article{xiao2017high, title={High-resolution remote sensing image retrieval based on CNNs from a dimensional perspective}, author={Xiao, Zhifeng and Long, Yang and Li, Deren and Wei, Chunshan and Tang, Gefu and Liu, Junyi}, journal={Remote Sensing}, volume={9}, number={7}, pages={725}, year={2017}, publisher={Multidisciplinary Digital Publishing Institute} } ``` ### Cite Meta-Album ``` @inproceedings{meta-album-2022, title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification}, author={Ullah, Ihsan and Carrion, Dustin and Escalera, Sergio and Guyon, Isabelle M and Huisman, Mike and Mohr, Felix and van Rijn, Jan N and Sun, Haozhe and Vanschoren, Joaquin and Vu, Phan Anh}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, url = {https://meta-album.github.io/}, year = {2022} } ``` ### More For more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https://meta-album.github.io/paper/Meta-Album.pdf) For details on the dataset preprocessing, please see the [[supplementary materials]](https://openreview.net/attachment?id=70_Wx-dON3q&name=supplementary_material) Supporting code can be found on our [[GitHub repo]](https://github.com/ihsaan-ullah/meta-album) Meta-Album on Papers with Code [[Meta-Album]](https://paperswithcode.com/dataset/meta-album) ### Other versions of this dataset [[Micro]](https://www.openml.org/d/44277) [[Mini]](https://www.openml.org/d/44307)

3 features

CATEGORY (target)string43 unique values
0 missing
FILE_NAMEstring43825 unique values
0 missing
SUPER_CATEGORYnumeric0 unique values
43825 missing

19 properties

43825
Number of instances (rows) of the dataset.
3
Number of attributes (columns) of the dataset.
43
Number of distinct values of the target attribute (if it is nominal).
43825
Number of missing values in the dataset.
43825
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
33.33
Percentage of numeric attributes.
7.14
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
3130
Number of instances belonging to the most frequent class.
0
Percentage of instances belonging to the least frequent class.
1
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
100
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
33.33
Percentage of missing values.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: CATEGORY
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