{
    "data_id": "44277",
    "name": "Meta_Album_RSD_Micro",
    "exact_name": "Meta_Album_RSD_Micro",
    "version": 1,
    "version_label": null,
    "description": "## **Meta-Album RSD Dataset (Micro)**\n***\nRSD46 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.  \n\n\n\n### **Dataset Details**\n![](https:\/\/meta-album.github.io\/assets\/img\/samples\/RSD.png)\n\n**Meta Album ID**: REM_SEN.RSD  \n**Meta Album URL**: [https:\/\/meta-album.github.io\/datasets\/RSD.html](https:\/\/meta-album.github.io\/datasets\/RSD.html)  \n**Domain ID**: REM_SEN  \n**Domain Name**: Remote Sensing  \n**Dataset ID**: RSD  \n**Dataset Name**: RSD  \n**Short Description**: Remote sensing dataset  \n**\\# Classes**: 20  \n**\\# Images**: 800  \n**Keywords**: remote sensing, satellite image, aerial image, land cover  \n**Data Format**: images  \n**Image size**: 128x128  \n\n**License (original data release)**: Open for research and non-profit purposes  \n**License (Meta-Album data release)**: CC BY-NC 4.0  \n**License URL (Meta-Album data release)**: [https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/](https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/)  \n\n**Source**: RSD46-WHU  \n**Source URL**: https:\/\/github.com\/RSIA-LIESMARS-WHU\/RSD46-WHU  \n  \n**Original Author**: Yang Long, Yiping Gong, Zhifeng Xiao, and Qing Liu, Deren Li, Chunshan Wei, Gefu Tang and Junyi Liu  \n**Original contact**: longyang@whu.edu.cn  \n\n**Meta Album author**: Phan Anh VU  \n**Created Date**: 01 March 2022  \n**Contact Name**: Ihsan Ullah  \n**Contact Email**: meta-album@chalearn.org  \n**Contact URL**: [https:\/\/meta-album.github.io\/](https:\/\/meta-album.github.io\/)  \n\n\n\n### **Cite this dataset**\n```\n@ARTICLE{7827088,\n  author={Long, Yang and Gong, Yiping and Xiao, Zhifeng and Liu, Qing},\n  journal={IEEE Transactions on Geoscience and Remote Sensing}, \n  title={Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks}, \n  year={2017},\n  volume={55},\n  number={5},\n  pages={2486-2498},\n  doi={10.1109\/TGRS.2016.2645610}}\n\n@article{xiao2017high,\n  title={High-resolution remote sensing image retrieval based on CNNs from a dimensional perspective},\n  author={Xiao, Zhifeng and Long, Yang and Li, Deren and Wei, Chunshan and Tang, Gefu and Liu, Junyi},\n  journal={Remote Sensing},\n  volume={9},\n  number={7},\n  pages={725},\n  year={2017},\n  publisher={Multidisciplinary Digital Publishing Institute}\n}\n```\n\n\n### **Cite Meta-Album**\n```\n@inproceedings{meta-album-2022,\n        title={Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification},\n        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},\n        booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n        url = {https:\/\/meta-album.github.io\/},\n        year = {2022}\n    }\n```\n\n\n### **More**\nFor more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]](https:\/\/meta-album.github.io\/paper\/Meta-Album.pdf)  \nFor details on the dataset preprocessing, please see the [[supplementary materials]](https:\/\/openreview.net\/attachment?id=70_Wx-dON3q&name=supplementary_material)  \nSupporting code can be found on our [[GitHub repo]](https:\/\/github.com\/ihsaan-ullah\/meta-album)  \nMeta-Album on Papers with Code [[Meta-Album]](https:\/\/paperswithcode.com\/dataset\/meta-album)  \n\n\n\n### **Other versions of this dataset**\n[[Mini]](https:\/\/www.openml.org\/d\/44307)  [[Extended]](https:\/\/www.openml.org\/d\/44341)  ",
    "format": "arff",
    "uploader": "Meta Album",
    "uploader_id": 30980,
    "visibility": "public",
    "creator": "\"Ihsan Ullah\"",
    "contributor": null,
    "date": "2022-10-28 11:29:50",
    "update_comment": null,
    "last_update": "2022-10-28 11:29:50",
    "licence": "CC BY-NC 4.0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22110977\/dataset",
    "default_target_attribute": "CATEGORY",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "Meta_Album_RSD_Micro",
            "## **Meta-Album RSD Dataset (Micro)** 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\/i "
        ],
        "weight": 5
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    "qualities": {
        "NumberOfInstances": 800,
        "NumberOfFeatures": 3,
        "NumberOfClasses": 20,
        "NumberOfMissingValues": 800,
        "NumberOfInstancesWithMissingValues": 800,
        "NumberOfNumericFeatures": 1,
        "NumberOfSymbolicFeatures": 0,
        "MajorityClassSize": 40,
        "MinorityClassPercentage": 5,
        "MinorityClassSize": 40,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 100,
        "PercentageOfMissingValues": 33.33333333333333,
        "AutoCorrelation": 1,
        "PercentageOfNumericFeatures": 33.33333333333333,
        "Dimensionality": 0.00375,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassPercentage": 5
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Text & Literature"
        }
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            "name": "CATEGORY",
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            "distinct": "20",
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    "nr_of_issues": 0,
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    "reach": 0,
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