Datasets:

evans44
/

cMars8k

---
license: cc-by-4.0
tags:
- space
- mars
- craters
---
## Hello!
**cMars8k** is a collection of 8,120 200x200 .jpg images of Martian craters, as well as their corresponding likelihoods of having subsurface ice at a depth of 0-1m.

**A short explanation of how the dataset was derived:**  
- The [THEMIS IR Day Map](https://www.mars.asu.edu/data/thm_dir/) was used to create this dataset, owing to its lightweight nature and scientific reputability.  
- [Robbins & Hynek's 2020 Mars crater database](https://craters.sjrdesign.net/) was used to source the geographic coordinates of the craters' (approximate) centers.  
- NASA's [SWIM Project 0-1m Ice Consistency map](https://swim.psi.edu/SWIM4MIMProducts.php) 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.

## 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.

## Accessing Data
This dataset can be accessed via:  
`!git clone https://huggingface.co/datasets/evans44/cMars8k`  
or via the Hugging Face API.

## Normalization
Many CNNs prefer normalized images as opposed to RGB. The following script loads the images directly from the downloaded folder into a pandas dataframe and performs said action.  
**!! Note:** within `metadata.csv`, the file_name for each image is in the format `images/crater_####`, and as such the `images` directory/folder does not need to be specified when accessing the images.
```python
import pandas as pd
import cv2
from concurrent.futures import ThreadPoolExecutor
import os
# Load metadata
metadata = pd.read_csv("cMars8k/metadata.csv")
IMAGES_DIR = "cMars8k"  # file_name column already includes "images/" prefix
def load_and_normalize(file_name):
    path = os.path.join(IMAGES_DIR, file_name)
    img = cv2.imread(path)                       # loads as BGR, uint8, shape (200, 200, 3)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)    # convert to RGB
    img = img.astype(np.float32) / 255.0          # normalize to [0, 1]
    return img
with ThreadPoolExecutor(max_workers=8) as executor:
    images = list(executor.map(load_and_normalize, metadata["file_name"]))
df_master = pd.DataFrame({                                # 3 columns:
    "file_name": metadata["file_name"],                   # file_name (over from metadata.csv)
    "image": images,                                      # image: the normalized version of the image
    "ice_prob": metadata["ice_likelihood"]                # ice_prob: probability of ice (over from metadata.csv/ice_likelihood)
})
print("Shape of df_master:")
print(df_master.shape)
print(df_master["image"].iloc[0].shape, df_master["image"].iloc[0].dtype)
```

	---
	license: cc-by-4.0
	tags:
	- space
	- mars
	- craters
	---

	## Hello!
	cMars8k is a collection of 8,120 200x200 .jpg images of Martian craters, as well as their corresponding likelihoods of having subsurface ice at a depth of 0-1m.

	A short explanation of how the dataset was derived:
	- The [THEMIS IR Day Map](https://www.mars.asu.edu/data/thm_dir/) was used to create this dataset, owing to its lightweight nature and scientific reputability.
	- [Robbins & Hynek's 2020 Mars crater database](https://craters.sjrdesign.net/) was used to source the geographic coordinates of the craters' (approximate) centers.
	- NASA's [SWIM Project 0-1m Ice Consistency map](https://swim.psi.edu/SWIM4MIMProducts.php) 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.

	## 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.

	## Accessing Data
	This dataset can be accessed via:
	`!git clone https://huggingface.co/datasets/evans44/cMars8k`
	or via the Hugging Face API.

	## Normalization
	Many CNNs prefer normalized images as opposed to RGB. The following script loads the images directly from the downloaded folder into a pandas dataframe and performs said action.
	!! Note: within `metadata.csv`, the file_name for each image is in the format `images/crater_####`, and as such the `images` directory/folder does not need to be specified when accessing the images.
	```python
	import pandas as pd
	import cv2
	from concurrent.futures import ThreadPoolExecutor
	import os

	# Load metadata
	metadata = pd.read_csv("cMars8k/metadata.csv")

	IMAGES_DIR = "cMars8k" # file_name column already includes "images/" prefix

	def load_and_normalize(file_name):
	path = os.path.join(IMAGES_DIR, file_name)
	img = cv2.imread(path) # loads as BGR, uint8, shape (200, 200, 3)
	img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # convert to RGB
	img = img.astype(np.float32) / 255.0 # normalize to [0, 1]
	return img

	with ThreadPoolExecutor(max_workers=8) as executor:
	images = list(executor.map(load_and_normalize, metadata["file_name"]))

	df_master = pd.DataFrame({ # 3 columns:
	"file_name": metadata["file_name"], # file_name (over from metadata.csv)
	"image": images, # image: the normalized version of the image
	"ice_prob": metadata["ice_likelihood"] # ice_prob: probability of ice (over from metadata.csv/ice_likelihood)
	})

	print("Shape of df_master:")
	print(df_master.shape)
	print(df_master["image"].iloc[0].shape, df_master["image"].iloc[0].dtype)

	```