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@@ -124,4 +124,16 @@ print("DEM shape:", sample["dem"].shape) # Expected: torch.Size(
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  print("Thermal shape:", sample["thermal_inertial"].shape)# Expected: torch.Size([1, 128, 128])
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  print("Grayscale shape:", sample["grayscale"].shape) # Expected: torch.Size([1, 128, 128])
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  print("Label shape:", sample["label"].shape) # Expected: torch.Size([1, 128, 128])
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
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  print("Thermal shape:", sample["thermal_inertial"].shape)# Expected: torch.Size([1, 128, 128])
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  print("Grayscale shape:", sample["grayscale"].shape) # Expected: torch.Size([1, 128, 128])
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  print("Label shape:", sample["label"].shape) # Expected: torch.Size([1, 128, 128])
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+ ```
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+
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+ ## References
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+
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+ ```
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+ @inproceedings{paheding2026mmlsv2,
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+ title={Mmlsv2: A multimodal dataset for martian landslide detection in remote sensing imagery},
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+ author={Paheding, S. and Reyes-Angulo, A. A. and Ramos, L. T. and Sappa, A. D. and KS, S. K. and Oommen, T.},
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+ booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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+ pages={10329--10338},
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+ year={2026}
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+ }
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  ```