{ "data_id": "44324", "name": "Meta_Album_RESISC_Extended", "exact_name": "Meta_Album_RESISC_Extended", "version": 1, "version_label": null, "description": "## **Meta-Album RESISC Dataset (Extended)**\n***\nRESISC45 dataset(https:\/\/gcheng-nwpu.github.io\/) gathers 700 RGB images of size 256x256 px for each of 45 scene categories. The data authors strive to provide a challenging dataset by increasing both within-class diversity and between-class similarity, as well as integrating many image variations. Even though RESISC45 does not propose a label hierarchy, it can be created from other common aerial image label organization scheme. We have preprocessed RESISC for Meta-Album by resizing the dataset to 128x128 px. \n\n\n\n### **Dataset Details**\n![](https:\/\/meta-album.github.io\/assets\/img\/samples\/RESISC.png)\n\n**Meta Album ID**: REM_SEN.RESISC \n**Meta Album URL**: [https:\/\/meta-album.github.io\/datasets\/RESISC.html](https:\/\/meta-album.github.io\/datasets\/RESISC.html) \n**Domain ID**: REM_SEN \n**Domain Name**: Remote Sensing \n**Dataset ID**: RESISC \n**Dataset Name**: RESISC \n**Short Description**: Remote sensing dataset \n**\\# Classes**: 45 \n**\\# Images**: 31500 \n**Keywords**: remote sensing, satellite image, aerial image, land cover \n**Data Format**: images \n**Image size**: 128x128 \n\n**License (original data release)**: CC-BY-NC 4.0 \n**License URL(original data release)**: https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/\n \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**: NWPU-RESISC45 Dataset \n**Source URL**: https:\/\/gcheng-nwpu.github.io\/ \n \n**Original Author**: Gong Cheng, Junwei Han, and Xiaoqiang Lu \n**Original contact**: chenggong1119@gmail.com \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{DBLP:journals\/corr\/ChengHL17,\n author = {Gong Cheng and Junwei Han and Xiaoqiang Lu},\n title = {Remote Sensing Image Scene Classification: Benchmark and State of the Art},\n journal = {CoRR},\n volume = {abs\/1703.00121},\n year = {2017},\n url = {http:\/\/arxiv.org\/abs\/1703.00121},\n eprinttype = {arXiv},\n eprint = {1703.00121},\n timestamp = {Mon, 02 Dec 2019 09:32:19 +0100},\n biburl = {https:\/\/dblp.org\/rec\/journals\/corr\/ChengHL17.bib},\n bibsource = {dblp computer science bibliography, https:\/\/dblp.org}\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[[Micro]](https:\/\/www.openml.org\/d\/44246) [[Mini]](https:\/\/www.openml.org\/d\/44290) ", "format": "arff", "uploader": "Meta Album", "uploader_id": 30980, "visibility": "public", "creator": "\"Ihsan Ullah\"", "contributor": null, "date": "2022-11-08 17:30:14", "update_comment": null, "last_update": "2022-11-08 17:30:14", "licence": "CC BY-NC 4.0", "status": "active", "error_message": null, "url": "https:\/\/api.openml.org\/data\/download\/22111038\/dataset", "default_target_attribute": "CATEGORY", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Meta_Album_RESISC_Extended", "## **Meta-Album RESISC Dataset (Extended)** RESISC45 dataset(https:\/\/gcheng-nwpu.github.io\/) gathers 700 RGB images of size 256x256 px for each of 45 scene categories. The data authors strive to provide a challenging dataset by increasing both within-class diversity and between-class similarity, as well as integrating many image variations. Even though RESISC45 does not propose a label hierarchy, it can be created from other common aerial image label organization scheme. We have preprocessed RES " ], "weight": 5 }, "qualities": { "NumberOfInstances": 31500, "NumberOfFeatures": 3, "NumberOfClasses": 45, "NumberOfMissingValues": 31500, "NumberOfInstancesWithMissingValues": 31500, "NumberOfNumericFeatures": 1, "NumberOfSymbolicFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 100, "PercentageOfMissingValues": 33.33333333333333, "AutoCorrelation": 1, "PercentageOfNumericFeatures": 33.33333333333333, "Dimensionality": 9.523809523809524e-5, "PercentageOfSymbolicFeatures": 0, "MajorityClassPercentage": 2.2222222222222223, "MajorityClassSize": 700, "MinorityClassPercentage": 2.2222222222222223, "MinorityClassSize": 700, "NumberOfBinaryFeatures": 0 }, "tags": [ { "uploader": "38960", "tag": "Statistics" } ], "features": [ { "name": "CATEGORY", "index": "1", "type": "string", "distinct": "45", "missing": "0", "target": "1" }, { "name": "FILE_NAME", "index": "0", "type": "string", "distinct": "31500", "missing": "0" }, { "name": "SUPER_CATEGORY", "index": "2", "type": "numeric", "distinct": "0", "missing": "31500", "min": "2147483647", "max": "0", "mean": "0", "stdev": "0" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 1, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 1 }