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## Meta-Album RSICB Dataset (Extended) * RSICB128 dataset ( covers 45 scene categories, assembling in total 36 000 images of resolution 128x128 px. The data authors select various locations around the world, and follow China's landuse classification standard. This collection has 2-level label hierarchy with 6 super-categories: agricultural land, construction land and facilities, transportation and facilities, water and water conservancy facilities, woodland, and other lands. The preprocessed version of RSICB is created by resizing the images into 128x128 px using an anti-aliasing filter. ### Dataset Details ![]( Meta Album ID: REM_SEN.RSICB Meta Album URL: []( Domain ID: REM_SEN Domain Name: Remote Sensing Dataset ID: RSICB Dataset Name: RSICB Short Description: Remote sensing dataset \# Classes: 45 \# Images: 36707 Keywords: remote sensing, satellite image, aerial image, land cover Data Format: images Image size: 128x128 License (original data release): Open for research purposes License (Meta-Album data release): CC BY-NC 4.0 License URL (Meta-Album data release): []( Source: RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data Source URL: Original Author: Haifeng Li, Xin Dou, Chao Tao, Zhixiang Hou, Jie Chen, Jian Peng, Min Deng, Ling Zhao Original contact: Meta Album author: Phan Anh VU Created Date: 01 March 2022 Contact Name: Ihsan Ullah Contact Email: Contact URL: []( ### Cite this dataset ``` @article{li2020RSI-CB, title={RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data}, author={Li, Haifeng and Dou, Xin and Tao, Chao and Wu, Zhixiang and Chen, Jie and Peng, Jian and Deng, Min and Zhao, Ling}, journal={Sensors}, DOI = {}, year={2020}, volume = {20}, number = {6}, pages = {1594}, type = {Journal Article} } ``` ### 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 = {}, year = {2022} } ``` ### More For more information on the Meta-Album dataset, please see the [[NeurIPS 2022 paper]]( For details on the dataset preprocessing, please see the [[supplementary materials]]( Supporting code can be found on our [[GitHub repo]]( Meta-Album on Papers with Code [[Meta-Album]]( ### Other versions of this dataset [[Micro]]( [[Mini]](

3 features

CATEGORY (target)string45 unique values
0 missing
FILE_NAMEstring36707 unique values
0 missing
SUPER_CATEGORYstring7 unique values
0 missing

19 properties

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

1 tasks

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