A comprehensive deep learning project for multi-class classification of post-earthquake satellite imagery, providing precise categorization of building damage types. Leveraging Convolutional Neural Networks, Focal Loss, and U-Net architecture, this work demonstrates advanced computer vision techniques for disaster assessment applications.
active 2023-10-23 → 2024-09-06 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 320 days from 2023-10-23 to 2024-09-06. Pushes: 11 total, peak 8 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 1 total, peak 1 in a day. Comments: 0 total, peak 0 in a day. Stars: 7 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
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- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
Recent activity
Latest issues, pull requests and releases
- Issue#1Jok3rL2024-06-16 11:16About Dataset
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 7 stars here means stars gained during the window, not the repo's star count.