README.md

metadata

dataset_info:
  features:
    - name: rgb
      dtype:
        array3_d:
          shape:
            - 3
            - 128
            - 128
          dtype: float32
    - name: dem
      dtype:
        array3_d:
          shape:
            - 1
            - 128
            - 128
          dtype: float32
    - name: slope
      dtype:
        array3_d:
          shape:
            - 1
            - 128
            - 128
          dtype: float32
    - name: thermal_inertial
      dtype:
        array3_d:
          shape:
            - 1
            - 128
            - 128
          dtype: float32
    - name: grayscale
      dtype:
        array3_d:
          shape:
            - 1
            - 128
            - 128
          dtype: float32
    - name: label
      dtype:
        array2_d:
          shape:
            - 128
            - 128
          dtype: float32
  splits:
    - name: train
      num_bytes: 245722740
      num_examples: 465
    - name: val
      num_bytes: 34876776
      num_examples: 66
    - name: test
      num_bytes: 70281988
      num_examples: 133
  download_size: 352286864
  dataset_size: 350881504
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test
        path: data/test-*
task_categories:
  - image-segmentation
language:
  - en
tags:
  - computer-vision
  - segmentation
  - remote-sensing
  - planetary-data
pretty_name: mmls-v2
size_categories:
  - 1K<n<10K
license: cc-by-4.0
citation: |-
  @inproceedings{paheding2026mmlsv2,
    title={Mmlsv2: A multimodal dataset for martian landslide detection in remote sensing imagery},
    author={Paheding, S. and Reyes-Angulo, A. A. and Ramos, L. T. and Sappa, A. D. and KS, S. K. and Oommen, T.},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages={10329--10338},
    year={2026}
  }

MMLS v2: Multimodal Martian Landslide Dataset

This repository hosts a Hugging Face compatible, Parquet-formatted version of the MMLS v2 dataset. It contains 128x128 multi-channel images for Martian landslide segmentation, featuring 7 data channels and 1 segmentation mask.

Disclaimer: I am not the original creator of this dataset. I am hosting this pre-processed version to allow researchers to easily stream the multi-channel arrays directly into PyTorch training pipelines without dealing with manual TIFF decoding. All credit for data collection and the original paper belongs to the authors of MMLSv2. Please see the Citation section below.

Dataset Structure

Each sample in the dataset is a dictionary containing pre-formatted, multi-dimensional arrays. To maintain 3D spatial consistency across all modalities, single-channel arrays include an explicit channel dimension of 1.

• rgb: (3, 128, 128) — Float32
• dem: (1, 128, 128) — Float32
• thermal_inertial: (1, 128, 128) — Float32
• grayscale: (1, 128, 128) — Float32
• label: (128, 128) — Float32 (Segmentation Mask)

Quickstart (PyTorch)

You can load this dataset directly into PyTorch tensors using the standard Hugging Face datasets library. No manual data conversion is required.

from datasets import load_dataset

# 1. Load a specific split from the Hub (train, val, or test)
dataset = load_dataset("sattwik21/mmls-v2", split="train")

# 2. Automatically format all arrays as native PyTorch Tensors
dataset = dataset.with_format("torch")

# 3. Pull a sample to verify
sample = dataset[0]

# 4. Verify tensor dimensions
print("RGB shape:", sample["rgb"].shape)                 # Expected: torch.Size([3, 128, 128])
print("DEM shape:", sample["dem"].shape)                 # Expected: torch.Size([1, 128, 128])
print("Thermal shape:", sample["thermal_inertial"].shape)# Expected: torch.Size([1, 128, 128])
print("Grayscale shape:", sample["grayscale"].shape)     # Expected: torch.Size([1, 128, 128])
print("Label shape:", sample["label"].shape)             # Expected: torch.Size([128, 128])

References

@inproceedings{paheding2026mmlsv2,
    title={Mmlsv2: A multimodal dataset for martian landslide detection in remote sensing imagery},
    author={Paheding, S. and Reyes-Angulo, A. A. and Ramos, L. T. and Sappa, A. D. and KS, S. K. and Oommen, T.},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages={10329--10338},
    year={2026}
  }