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MMLSv2 is a multimodal remote sensing dataset for Martian surface landslide detection, containing 7 bands including RGB, elevation model, slope, thermal inertia, etc. It has been accepted by the CVPR 2026 AI4Space Workshop and serves as the official dataset for the MARS-LS Challenge.
Section 01
MMLSv2 is a multimodal remote sensing dataset for Martian surface landslide detection, containing 7 bands including RGB, elevation model, slope, thermal inertia, etc. It has been accepted by the CVPR 2026 AI4Space Workshop and serves as the official dataset for the MARS-LS Challenge.
Section 02
Section 03
Martian geological research is an important topic in human deep space exploration. Landslide landforms are widely distributed on the Martian surface, and these landslides record clues about Mars' climate history and hydrological activities. Accurate identification and segmentation of Martian landslide areas have important scientific value for understanding Martian geological evolution and assessing the risk of site selection for future manned missions.
However, Martian landslide detection faces unique challenges:
Section 04
MMLSv2 (Multimodal Dataset for Martian Landslide Detection) is a multimodal remote sensing dataset specifically designed for the Martian landslide segmentation task. This dataset has been accepted by the CVPR 2026 AI4Space Workshop and serves as the official dataset for the first Martian Landslide Segmentation Challenge (MARS-LS).
Section 05
The dataset contains remote sensing data of 7 bands:
| Band | Content | Description |
|---|---|---|
| B1 | Red | Red visible light |
| B2 | Green | Green visible light |
| B3 | Blue | Blue visible light |
| B4 | DEM | Digital Elevation Model |
| B5 | Slope | Slope data |
| B6 | Thermal Inertia | Thermal Inertia |
| B7 | Grayscale | Grayscale channel |
Multi-band fusion allows models to simultaneously utilize spectral information, topographic features, and thermophysical properties, significantly improving the accuracy of landslide identification.
Section 06
The dataset contains a total of 940 images, divided as follows:
| Division | Number of Images | Average Foreground Ratio | Foreground Ratio Range |
|---|---|---|---|
| Training Set | 465 | 35.41% | 0.02% - 99.52% |
| Validation Set | 66 | 31.53% | 0.08% - 90.32% |
| Test Set | 133 | 33.82% | 0.10% - 90.67% |
| Independent Test Set | 276 | 21.83% | 0.01% - 71.95% |
The independent test set comes from geographically disjoint regions of the main dataset and is specifically used to evaluate the spatial generalization ability of models.
Section 07
Each sample is a multi-channel image of size (128, 128, 7) with data type float32, and pixel values are normalized to the range [0.0, 1.0]. The corresponding mask is a single-channel (128, 128) uint8 image, with pixel values 0 or 1 representing background or landslide areas respectively.
Section 08
The paper reports the performance of 6 mainstream segmentation models on MMLSv2:
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