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

# MMLS v2: Multimodal Martian Landslide Dataset

![image](https://cdn-uploads.huggingface.co/production/uploads/658b1ad65c6fb5d5e311d7a3/zsLLj1lvwaaWtgOuVEF2m.png)

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.

```python
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}
  }
```