I initially planned to use 4 simple categories (crater, dusty, rocky, sandy), but I ended up with 7 scientific categories from the HiRISE orbiter dataset: bright dune, crater, dark dune, impact ejecta, slope streak, spider, and swiss cheese.

The main challenge was that the official NASA datasets (AI4Mars) had over 50,000 images and were too large to download (around 16-20 GB). Direct download kept failing, and the dataset required heavy preprocessing because it was semantic segmentation, not simple image classification. So instead, I used a smaller HiRISE dataset from Kaggle. The trade-off is that these are orbital telescope images, not rover photos, so the terrain looks different. Also, some categories like spider and swiss cheese are very rare, which makes the dataset imbalanced, and the model might struggle to learn them properly.