Satellite Image Object Detection with YOLOv11:
From Labeling to Model Deployment

About Satellite Image Object Detection with YOLOv11:
From Labeling to Model Deployment

Join us for an immersive, hands-on workshop where you’ll learn to harness the power of YOLOv11 for object detection in satellite imagery. This workshop will cover the entire object detection workflow, from labeling satellite images to training YOLOv11, and will introduce key concepts in deep learning and remote sensing. Whether you're new to the field or looking to enhance your skills, you'll gain practical experience in applying cutting-edge AI techniques to real-world satellite image analysis, such as detecting buildings, roads, vegetation, and more.

Workshop Level: Intermediate Level
Workshop Language: English
Workshop Requirement: Gmail Account & Basic knowledge of Python programming

Highlights

  1. Introduction to Deep Learning: Understand the basics of deep learning, its importance in object detection, and how it is applied to satellite imagery.
  2. Introduction to Remote Sensing: Learn about the fundamentals of remote sensing, satellite image acquisition, and how remote sensing data is used for object detection.
  3. Labeling Satellite Imagery: Step-by-step guide on how to label objects in satellite images using tools like LabelImg or Roboflow.
  4. Training YOLOv11: Hands-on experience in setting up, training, and evaluating YOLOv11 models for object detection in satellite imagery.
  5. Data Augmentation: Techniques for expanding labeled datasets through data augmentation, which is crucial for improving model performance.
  6. Evaluation and Optimization: Learn how to assess model performance and apply strategies to optimize detection accuracy.
  7. Interactive Learning: Collaborate with peers, receive guidance from instructors, and build your own object detection models.
  8. Networking: Meet professionals and enthusiasts in deep learning, remote sensing, and machine learning.
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