John-Pinto/Object_Detection_Satellite_Imagery_Yolov8_DIOR
Building a Yolov8n model from scratch and performing object detection in optical remote sensing images and videos.
This project helps defense analysts, urban planners, or environmental scientists automatically identify objects in overhead satellite imagery. You input satellite images or video, and it outputs bounding boxes and labels for 20 different object types, like vehicles, buildings, or aircraft. This is for professionals who need to quickly survey large geographical areas for specific features.
107 stars. No commits in the last 6 months.
Use this if you need to rapidly detect and classify objects from optical remote sensing images or videos for applications like land use mapping or disaster assessment.
Not ideal if your primary data source is ground-level photography or if you require detection of objects not typically visible from an overhead perspective.
Stars
107
Forks
17
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 05, 2023
Commits (30d)
0
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