Datasets:

Mirali33
/

mb-mmls

annotations_creators:
  - expert-generated
language_creators:
  - found
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - image-segmentation
task_ids:
  - semantic-segmentation
pretty_name: mb-mmls

mb-mmls

A segmentation dataset for planetary science applications.

Dataset Metadata

License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
Version: 1.0
Date Published: 2025-10-23
Cite As: TBD

Classes

This dataset contains the following classes:

0: Background
1: Landslide

Directory Structure

The dataset follows this structure:

dataset/
  ├── train/
  │   ├── images/  # Image files
  │   └── masks/   # Segmentation masks
  ├── val/
  │   ├── images/  # Image files
  │   └── masks/   # Segmentation masks
  ├── test/
  │   ├── images/  # Image files
  │   └── masks/   # Segmentation masks

Statistics

train: 275 images
val: 31 images
test: 256 images
partition_train_0.02x_partition: 5 images
partition_train_0.50x_partition: 137 images
partition_train_0.10x_partition: 27 images
partition_train_0.25x_partition: 68 images
partition_train_0.05x_partition: 13 images
partition_train_0.01x_partition: 2 images
partition_train_0.20x_partition: 55 images

Usage

from datasets import load_dataset

dataset = load_dataset("Mirali33/mb-mmls")

Format

Each example in the dataset has the following format:

{
  'image': Image(...),      # PIL image
  'mask': Image(...),       # PIL image of the segmentation mask
  'width': int,             # Width of the image
  'height': int,            # Height of the image
  'class_labels': [str,...] # List of class names present in the mask
}