Articles | Volume 17, issue 9
https://doi.org/10.5194/essd-17-4835-2025
https://doi.org/10.5194/essd-17-4835-2025
Data description paper
 | 
26 Sep 2025
Data description paper |  | 26 Sep 2025

A large-scale image–text dataset benchmark for farmland segmentation

Chao Tao, Dandan Zhong, Weiliang Mu, Zhuofei Du, and Haiyang Wu

Cited articles

Cheng, Q., Huang, H., Xu, Y., Zhou, Y., Li, H., and Wang, Z.: NWPU-Captions Dataset and MLCA-Net for Remote Sensing Image Captioning, IEEE T. Geosci. Remote, 60, 1–19, https://doi.org/10.1109/TGRS.2022.3201474, 2022. 
Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R.: DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 172–182, https://doi.org/10.1109/CVPRW.2018.00031, 2018. 
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.04805, 24 May 2019. 
Duan, D., Sun, X., Liang, S., Sun, J., Fan, L., Chen, H., Xia, L., Zhao, F., Yang, W., and Yang, P.: Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China, Remote Sens.-Basel, 14, 1250, https://doi.org/10.3390/rs14051250, 2022. 
Download
Short summary
We construct FarmSeg-VL, the first high-quality image–text dataset for farmland segmentation. It covers eight agricultural regions across four seasons in China, offering extensive spatiotemporal coverage and fine-grained annotations. This dataset fills the gap in remote sensing image–text datasets for farmland, alleviates the challenge of spatiotemporal heterogeneity in farmland segmentation, and provides valuable data to support large-scale farmland monitoring and mapping.
Share
Altmetrics
Final-revised paper
Preprint