1 dataset of high-resolution (GF) remote sensing imagery and implementation scripts for semantic segmentation and land cover classification. The dataset supports deep learning model development specifically within the PyTorch framework.
Use Cases
- Train semantic segmentation models for land cover classification using high-resolution imagery
- Develop PyTorch-based pipelines for automated feature extraction from satellite data
- Perform pixel-level classification using land cover labels and remote sensing imagery
Strengths
- Focuses on high-resolution (GF) remote sensing imagery
- Includes labels for semantic segmentation and land cover classification
- Optimized for use with the PyTorch deep learning framework