Hugging Face's logo

Datasets:
Project-AgML
/
simta_rice_mapping_classification

Tasks:
Image Classification
Modalities:
Text
Formats:
parquet
Size:
1K - 10K
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
simta_rice_mapping_classification
File size: 2,178 Bytes
7c1bc2b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ba223ad
 
 
 
 
7c1bc2b
ba223ad
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
---
dataset_info:
  features:
  - name: bands
    dtype: binary
  - name: bands_shape
    list: int64
  - name: bands_dtype
    dtype: string
  - name: band_metadata
    dtype: string
  - name: band_order
    list: string
  - name: label
    dtype:
      class_label:
        names:
          '0': 2019Canifornia
          '1': EImage
          '2': ThImage
          '3': TImage
  splits:
  - name: train
    num_bytes: 5400475005
    num_examples: 3024
  download_size: 4236996494
  dataset_size: 5400475005
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---
# Simta Rice Mapping Classification

This dataset provides real SAR satellite imagery of rice fields collected across the Arkansas River Basin, Sacramento Valley, and Suihua regions using Sentinel-1 between 2017 and 2019. It captures multi-temporal rice cultivation patterns in agricultural field environments across the United States and China, offering valuable data for computer vision research in crop monitoring and mapping. The dataset contains 3,024 images across 4 classes: 2019Canifornia, EImage, ThImage, TImage.  
Images per class:
- 2019Canifornia: 375
- EImage: 706
- ThImage: 884
- TImage: 1,059

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{ren2026simta,
  title={SimTA: A Dual-Polarization SAR Time-Series Rice Field Mapping Model Based on Deep Feature-Level Fusion and Spatiotemporal Attention},
  author={Ren, Dong and Liang, Jiaxuan and Liu, Li and Wei, Pengliang and Yang, Lingbo and Wang, Lu and Sun, Hang and Zhang, Kehan and Qiu, Bingwen and Liu, Weiwei and Huang, Jingfeng},
  journal={Remote Sensing},
  volume={18},
  pages={1237},
  year={2026},
  publisher={MDPI}
}
```


The dataset itself can be cited as:

Liu, L., Deng, X., &amp; Quan, T. (2025). *Rice Mapping Based on SAR Imagery – Arkansas River Basin, Sacramento Region* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17997718


*This dataset was reformatted from its original format to match HuggingFace standards.*