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---
dataset_info:
features:
- name: image
dtype: image
- name: labels
sequence: string
splits:
- name: train
num_bytes: 803214220.7904668
num_examples: 65489
- name: validation
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num_examples: 21830
- name: test
num_bytes: 268741440.4589508
num_examples: 21842
download_size: 1320536329
dataset_size: 1338152914.037
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# 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
- **Repository:** https://github.com/cugbrs/MLRSNet
- **Paper :** https://www.sciencedirect.com/science/article/abs/pii/S0924271620302677
## 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
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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 |