evans44/cMars12k

Hello!

cMars12k is a collection of 11,355 200x200 .jpg images of Martian craters, as well as their corresponding likelihoods of having subsurface ice at a depth of 0-1m.
This is an updated and more balanced version of cMars8k.

A short explanation of how the dataset was derived:

• The THEMIS IR Day Map was used to create this dataset, owing to its lightweight nature and scientific reputability.
• Robbins & Hynek's 2020 Mars crater database was used to source the geographic coordinates of the craters' (approximate) centers.
• NASA's SWIM Project 0-1m Ice Consistency map was used to source the likelihood of subsurface ice at the location of each crater.
• All of these individual datasets were combined together by overlaying them on top of each other inside of QGIS, and then utilizing a python script to mass-export the images.
• Each category contains exactly 2,000 images, except for very_likely_confirmed, which contains 1,355.

Data Formatting

As stated, each image is exactly 200x200 pixels and is stored as a .jpg file within cMars8k/images. The ice_likelihood value is a float64 ranging from approximately -0.375 to 1.000, with 1.000 representing an extremely high probability or confirmed presence of subsurface ice.

Each image has been sorted into one of six classes based on the ice_likelihood value, as follows:
-0.375 to -0.20: very_unlikely
-0.20 to 0.00: unlikely
0.00 to 0.20: possible
0.20 to 0.50: somewhat_likely
0.50 to 0.80: likely
0.80 to 1.00: very_likely_confirmed

Binary classifiers are recommended to use >=0.50 as the cutoff for Likely and <0.50 for Unlikely, reflective of the original NASA/SWIM Project data. Note that this split is imbalanced (8000 Unlikely > 3355 Likely), however has shown successful results in training with a from-scratch binary classifier as well as one based on the ResNet50 architecture.

Accessing Data

This dataset can be accessed via:

git clone https://huggingface.co/datasets/evans44/cMars12k

or via the Hugging Face API.