Detecting Objects in Satellite Imagery with YOLOv8: xView + DOTA to YOLO in Practice
Article summary
Quick briefing — cleaned from the original RSS feed
Satellite imagery breaks most of the assumptions object detectors are built on. Objects are tiny (a car is 10–15 px in a 3000×3000 tile), classes are wildly imbalanced, and the annotations don't come in anything close to YOLO format. I spent a while building dl_xview_yolo — a YOLOv8 pipeline for detecting planes, ships, vehicles, bridges and storage tanks in aerial and satellite images, trained on xView and DOTA . This post is about the parts that actually took time: the data conversion, the…
1Key Takeaways
- Satellite imagery breaks most of the assumptions object detectors are built on.
- Objects are tiny (a car is 10–15 px in a 3000×3000 tile), classes are wildly imbalanced, and the annotations don't come in anything close to YOLO format.
- I spent a while building dl_xview_yolo — a YOLOv8 pipeline for detecting planes, ships, vehicles, bridges and storage tanks in aerial and satellite images, trained on xView and DOTA .
- This post is about the parts that actually took time: the data conversion, the….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that satellite imagery breaks most of the assumptions object detectors are built on.
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