An overhead and satellite analog to the classic MNIST Digit Recognizer dataset. Also include CSV versions for each image to match with other statistical and computer vision methods that extract features or build models without employ pixel-based deep learning.
Consists of training and test splits (9:1) and includes 1000 images per class as 28x28 pixel grayscale versions