This tutorial is part of a series where I’ll explore deep learning applications across various domains, each with its own project.
The focus here is on learning deep Learning and image classification.
We could have chosen to classify cats, dogs, or flowers, but for this project, we opted to work with satellite images as they provide a unique and exciting challenge.
For this, we’ll use the EuroSAT_RGB dataset, generously shared by my friend Blanchon on Hugging Face. A special thanks to him for making this dataset available to the community, enabling projects like this.
In this case, the goal is to teach a model to categorize different types of environments — like forests, rivers, or urban areas — based on satellite imagery.
However, the potential applications of this type of technology extend far beyond satellite images, including:
- Medical imaging, like detecting tumors in X-rays or MRIs.
- Autonomous vehicles, enabling them to recognize roads, obstacles, and pedestrians.
- Retail, for customer behavior analysis through video footage.
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