Zhao et al. (2025) SEV-Field: A Crop Field Extraction Framework for High-Resolution Imagery via Semantic Segmentation and Boundary Connection

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-12-01
• Authors: Lingyuan Zhao, Qun Wang, Kuang Zhou, Zifei Luo, Bo Yang, Yan Zhang
• DOI: 10.1109/jstars.2025.3638898

Research Groups

[Information not available in the provided text.]

Short Summary

[Information not available in the provided text.]

Objective

• To develop a framework (SEV-Field) for extracting crop fields from high-resolution imagery using semantic segmentation and boundary connection.

Study Configuration

• Spatial Scale: [Information not available in the provided text.]
• Temporal Scale: [Information not available in the provided text.]

Methodology and Data

• Models used: Semantic segmentation, boundary connection (inferred from title).
• Data sources: High-resolution imagery (inferred from title).

Main Results

• [Information not available in the provided text.]

Contributions

• [Information not available in the provided text.]

Funding

• [Information not available in the provided text.]

Citation

@article{Zhao2025SEVField,
  author = {Zhao, Lingyuan and Wang, Qun and Zhou, Kuang and Luo, Zifei and Yang, Bo and Zhang, Yan},
  title = {SEV-Field: A Crop Field Extraction Framework for High-Resolution Imagery via Semantic Segmentation and Boundary Connection},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3638898},
  url = {https://doi.org/10.1109/jstars.2025.3638898}
}