Hugging Face's logo

Datasets:
vigneshwar472
/
MLRS-Net-for-modelling

Modalities:
Image
Text
Formats:
parquet
Size:
100K - 1M
Libraries:
Datasets
Dask
Dataset card Viewer Files Community
Dataset Viewer
Auto-converted to Parquet
Full Screen
image
imagewidth (px)
256
600
labels
sequencelengths
0
13
[ "bare soil", "forest", "trail", "trees" ]
[ "buildings", "cars", "park", "trail", "trees", "water" ]
[ "desert", "sand" ]
[ "bare soil", "buildings", "cars", "pavement", "tennis court", "trees" ]
[ "bare soil", "transmission tower" ]
[ "bare soil", "basketball court", "cars", "pavement", "road" ]
[ "grass", "pavement", "tennis court", "trees" ]
[ "basketball court", "buildings", "cars", "pavement", "road", "trees" ]
[ "buildings", "cars", "crosswalk", "pavement", "road", "roundabout", "trees" ]
[ "chaparral", "sand" ]
[ "field", "gully", "trail" ]
[ "bare soil", "buildings", "cars", "grass", "parking lot", "pavement", "road", "trees" ]
[ "grass", "mountain", "trees" ]
[ "bare soil", "cars", "freeway", "grass", "pavement", "road", "trees" ]
[ "grass", "pavement", "tennis court", "trees" ]
[ "bare soil", "cars", "freeway", "grass", "pavement", "road", "trees" ]
[ "basketball court", "buildings", "trees" ]
[ "buildings", "chaparral", "pavement", "road", "sand", "sparse residential area", "trees" ]
[ "buildings", "chaparral", "pavement", "road", "sand", "sparse residential area", "trees" ]
[ "buildings", "cars", "pavement", "trees", "parkway" ]
[ "buildings", "cars", "mobile home", "pavement", "trees" ]
[ "buildings", "cars", "grass", "overpass", "parking lot", "pavement", "road", "trees" ]
[ "bare soil", "buildings", "cars", "grass", "overpass", "parking lot", "pavement", "road", "trees" ]
[ "airplane", "pavement" ]
[ "field", "gully", "trail" ]
[ "bare soil", "baseball diamond", "buildings", "grass", "road", "trees" ]
[ "cars", "grass", "pavement", "road", "tennis court", "trees" ]
[ "field", "gully", "trail" ]
[ "buildings", "cars", "dense residential area", "grass", "pavement", "road", "swimming pool", "trees", "water" ]
[ "buildings", "pavement", "railway", "railway station", "trees" ]
[ "bare soil", "buildings", "pavement", "road", "tanks", "trees" ]
[ "bare soil", "grass", "lake", "water" ]
[ "bare soil" ]
[ "buildings", "grass", "pavement", "swimming pool", "water" ]
[ "bare soil", "forest", "trail", "trees" ]
[ "water", "wetland" ]
[ "trees", "grass", "island", "sea", "water" ]
[ "grass", "pavement", "swimming pool", "trees", "water" ]
[ "bare soil", "baseball diamond", "buildings", "grass", "road", "trees" ]
[ "cloud", "water" ]
[ "containers", "pavement" ]
[ "beach", "sand", "sea", "trees", "water" ]
[ "bare soil", "baseball diamond", "buildings", "grass", "parking lot", "road", "trees" ]
[ "bare soil", "field", "greenhouse", "trail", "trees" ]
[ "bare soil", "buildings", "cars", "mobile home", "pavement", "trees" ]
[ "bare soil", "buildings", "pavement", "swimming pool", "tennis court", "trees", "water" ]
[ "forest", "trees" ]
[ "bare soil", "field", "grass", "river", "sand", "trees", "water" ]
[ "bare soil", "bridge", "buildings", "cars", "grass", "pavement", "road", "trees", "water" ]
[ "buildings", "cars", "factory", "pavement", "trees" ]
[ "bare soil", "baseball diamond", "buildings", "grass", "trees" ]
[ "grass", "trees", "cars", "pavement", "road", "crosswalk", "intersection" ]
[ "bare soil", "factory", "buildings", "grass", "trees", "cars", "pavement", "road", "crosswalk", "intersection" ]
[ "desert", "sand", "chaparral" ]
[ "buildings", "chaparral", "sand", "sparse residential area", "trees" ]
[ "desert", "sand", "chaparral" ]
[ "bare soil", "buildings", "cars", "dock", "grass", "habor", "pavement", "road", "ships", "water" ]
[ "bare soil", "forest", "trees" ]
[ "buildings", "factory", "pavement", "trees" ]
[ "bare soil", "buildings", "grass", "pavement", "runway", "trees", "water" ]
[ "buildings", "cars", "factory", "grass", "pavement" ]
[ "bare soil", "buildings", "pavement", "cars", "parking lot", "railway", "railway station" ]
[ "bare soil", "buildings", "cars", "grass", "parking lot", "pavement", "road", "trees" ]
[ "field", "trees" ]
[ "bare soil", "lake", "mountain", "water" ]
[ "buildings", "cars", "parking lot", "pavement" ]
[ "bare soil", "lake", "mountain", "water" ]
[ "bare soil", "buildings", "pavement", "railway", "railway station", "grass", "trees" ]
[ "forest", "trees" ]
[ "grass", "mountain", "snow", "snowberg" ]
[ "bare soil", "cars", "freeway", "grass", "pavement", "road", "trees" ]
[ "bare soil", "buildings", "field", "grass", "river", "sand", "trees", "water" ]
[ "cars", "grass", "railway" ]
[ "buildings", "cars", "pavement", "trees", "parkway" ]
[ "mountain", "snow", "snowberg", "water" ]
[ "buildings", "cars", "pavement", "road", "tanks", "trees" ]
[ "bare soil", "buildings", "cars", "grass", "park", "pavement", "road", "trail", "trees", "water" ]
[ "buildings", "cars", "grass", "pavement", "road", "tennis court", "trees" ]
[ "bare soil", "buildings", "cloud" ]
[ "bare soil", "grass", "transmission tower" ]
[ "bare soil", "basketball court", "buildings", "grass", "pavement", "road", "track", "trees", "water" ]
[ "bare soil", "water", "wetland" ]
[ "bare soil", "buildings", "cars", "mobile home", "pavement", "trees" ]
[ "containers", "pavement", "road" ]
[ "buildings", "grass", "pavement", "road", "swimming pool", "tennis court", "trees", "water" ]
[ "buildings", "cars", "dense residential area", "grass", "pavement", "road", "trees" ]
[ "bare soil", "bridge", "buildings", "field", "grass", "river", "sand", "trees", "water" ]
[ "field", "trail", "trees" ]
[ "airplane", "airport", "buildings", "cars", "pavement" ]
[ "basketball court", "buildings", "grass", "pavement", "road", "track", "trees" ]
[ "bridge", "buildings", "cars", "grass", "pavement", "road", "trees", "water" ]
[ "bare soil", "buildings", "grass", "pavement", "road", "track", "trees" ]
[ "bare soil", "grass", "transmission tower" ]
[ "bare soil", "buildings", "pavement", "railway", "railway station", "trees" ]
[ "bare soil", "buildings", "cars", "grass", "park", "pavement", "road", "trail", "trees" ]
[ "buildings", "grass", "pavement", "swimming pool", "tennis court", "trees", "water" ]
[ "beach", "buildings", "sand", "sea", "ships", "water" ]
[ "cars", "parking lot", "pavement" ]
[ "cars", "grass", "pavement", "railway", "road", "trees" ]
[ "airport", "bare soil", "buildings", "grass", "pavement", "runway", "trees", "water" ]
End of preview. Expand in Dataset Viewer.

Dataset Card for MLRS Net

MLRSNet is a multi-label high spatial resolution remote sensing dataset for semantic scene understanding. It provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.

Dataset Sources

Uses

The dataset has many use cases in remote sensing applications. It is crucial to get all the tags of the image. The algorithms trained on the model make things simpler in the applications.

Direct Use

  1. Multilabel image classification
  2. Remote sensing applications
  3. Deep Learning
  4. Scene Understanding

Dataset Structure

There are three splits in total : train, validation, test

It is important to note that the entries are shuffled and there is no chance of having a bias.

Dataset Creation

Source Data

I have got the dataset from https://data.mendeley.com/datasets/7j9bv9vwsx/3

Data Processing

I have converted it into hugging face dataset via this notebook https://colab.research.google.com/drive/12ONm4rToN-DE7CkHIs86gNN6w2iKIUJG?usp=sharing

Who are the contributors?

Xiaoman Qi,Panpan Zhu,Yuebin Wang,Liqiang Zhang,Junhuan Peng,Mengfan Wu,Jialong Chen,Xudong Zhao,Ning Zang,P.Takis Mathiopoulos

Citation

Qi, Xiaoman; Zhu, Panpan; Wang, Yuebin; Zhang, Liqiang; Peng, Junhuan; Wu, Mengfan; Chen, Jialong; Zhao, Xudong; Zang, Ning; Mathiopoulos, P.Takis (2021), “MLRSNet: A Multi-label High Spatial Resolution Remote Sensing Dataset for Semantic Scene Understanding”, Mendeley Data, V3, doi: 10.17632/7j9bv9vwsx.3

Dataset Card Authors

https://huggingface.co/vigneshwar472

Dataset Card Contact

Email : vigneshwar472@gmail.com

Downloads last month
59
Size of downloaded dataset files:
1.32 GB
Size of the auto-converted Parquet files:
1.32 GB
Number of rows:
109,161