Semantic Segmentation Model Results on UAVid Remote Sensing Images

Featuring segmentation results from multiple models applied to the UAVid dataset. The data is stored in an XLS file sized 9.5 KB. It was authored by Xin Wang and last updated in March 2026.

Use Cases

• Compare model performance on semantic segmentation tasks for remote sensing images.
• Analyze segmentation results for dense small targets and complex boundary shapes.
• Evaluate how models integrate global and shallow spatial features for precise segmentation.
• Study the effect of multi-scale features extracted from wide imaging ranges on segmentation accuracy.

Strengths

• Data is licensed under CC BY 4.0, allowing for open sharing and adaptation.
• Focuses on semantic segmentation for remote sensing, a domain with specific challenges like dense small targets.
• Includes results from multiple models, enabling comparative analysis.

Limitations

• The dataset is very small at 9.5 KB, indicating limited scope and likely a summary or sample of results.
• No column names, row counts, or sample data are provided, limiting understanding of the data structure.
• The description is minimal, lacking details on the specific models evaluated or performance metrics.

Provenance

Source

figshare, authored by Xin Wang.

Collection Method

Segmentation results generated by applying different models to the UAVid dataset.

Freshness

Last updated March 2026.