Zhang (2025) An Automated Labeling Method for Lakes from Remote Sensing Images Guided by Visual and Structural Features

Identification

• Journal: Mendeley Data
• Year: 2025
• Date: 2025-12-11
• Authors: Zhang, Yiqing
• DOI: 10.17632/c8z6v284s8

Research Groups

Not explicitly detailed in the provided text, but Yiqing Zhang is listed as the contributor.

Short Summary

This paper introduces an automated method for labeling lakes in remote sensing images using visual and structural features, and provides a corresponding dataset for its evaluation.

Objective

• To develop and evaluate an automated method for identifying and labeling lakes in remote sensing imagery based on visual and structural features.

Study Configuration

• Spatial Scale: Pixel-level within remote sensing image patches, capturing diverse lake types with variations in size, morphology, brightness, and background complexity.
• Temporal Scale: Not specified.

Methodology and Data

• Models used: An automated lake-labeling framework guided by visual and structural features.
• Data sources: Raw remote sensing image patches; corresponding lake masks (ground truth labels).

Main Results

Not explicitly detailed in the provided data description, as this text describes the dataset used for evaluation rather than the evaluation results themselves.

Contributions

Development of an automated, feature-guided method for lake labeling in remote sensing images, along with a dedicated dataset for its evaluation.

Funding

Not specified in the provided text.

Citation

@article{Zhang2025Automated,
  author = {Zhang, Yiqing},
  title = {An Automated Labeling Method for Lakes from Remote Sensing Images Guided by Visual and Structural Features},
  journal = {Mendeley Data},
  year = {2025},
  doi = {10.17632/c8z6v284s8},
  url = {https://doi.org/10.17632/c8z6v284s8}
}