nasa-ibm-ai4science/Sombench-IMP-Segmentation
SomBench Benchmark: Irregular Mare Patch (IMP) Segmentation Science theme: Volcanic history Task: Binary semantic segmentation Dataset Summary A binary semantic-segmentation benchmark for irregular mare patches (IMPs): rare, morphologically subtle features interpreted as unusually young volcanic landforms. Each sample is an LROC NAC image tile paired with a binary IMP mask (IMP vs. background). The set is derived from published IMP polygon annotations, framed as a… See the full description on the dataset page: https://huggingface.co/datasets/nasa-ibm-ai4science/Sombench-IMP-Segmentation.
SomBench Benchmark: Irregular Mare Patch (IMP) Segmentation
Science theme: Volcanic history
Task: Binary semantic segmentation
Dataset Summary
A binary semantic-segmentation benchmark for irregular mare patches (IMPs): rare, morphologically subtle features interpreted as unusually young volcanic landforms. Each sample is an LROC NAC image tile paired with a binary IMP mask (IMP vs. background). The set is derived from published IMP polygon annotations, framed as a coarse-to-fine mapping problem, and split into train / validation / test.
Dataset Structure
Directory layout
IMP_dataset/
├── README.md # dataset notes (source)
├── all/ # image + mask tiles
└── *.txt # split file listsEach sample is a pair of GeoTIFFs sharing a common stem:
{PRODUCT}.ech.cog__target_{id}__idx_{id}_p0_img.tif # NAC image tile
{PRODUCT}.ech.cog__target_{id}__idx_{id}_p0_mask.tif # binary IMP mask (used for training/eval)- `{PRODUCT}`: source LROC NAC EDR id (e.g.
M1119107675LE), calibrated, map-projected and stored as a Cloud-Optimized GeoTIFF (.ech.cog). - `{id}`: id for the sample.
- `_mask.tif`: the label used for training/evaluation.
Contents
Known Limitations
- Noisy labels: instrument pointing/geolocation uncertainty can misalign IMP annotations by tens of meters; a subset was manually re-aligned, but residual offsets remain.
- IMP polygons exceeding the 256 m patch side length (≈ 100 features) were excluded.
Citation
@article{fraccaro2026lfm,
title = {Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing},
author = {Fraccaro, Paolo and Nyirjesy, Gabby and Szwarcman, Daniela and Patil, Himanshu
and Gaur, Vishal and Lal, Rohit and Slank, Rachel A. and Dawson, Geoffrey
and Debary, Hiyam and Dionelis, Nikolaos and Barker, Michael K. and Annex, Andrew
and Viswanathan, Vishnu and Morse, Zachary and Schaefer, Ethan I. and Kumar, Ankur
and Watson, Campbell D. and Dawson-Rigas, Rebekah I. and Maskey, Manil
and Roy, Sujit and Ramachandran, Rahul and Bernab\'e-Moreno, Juan},
year = {2026}
howpublished = {\url{https://huggingface.co/collections/nasa-ibm-ai4science/nasa-ibm-lunar-fm-and-downstream-models}}
}
@misc{sombench2026collection,
author = {Patil, Himanshu and Nyirjesy, Gabby and Slank, Rachel A. and Gaur, Vishal
and Szwarcman, Daniela and Fraccaro, Paolo and Dionelis, Nikolaos and Barker, Michael K.
and Annex, Andrew and Viswanathan, Vishnu and Morse, Zachary and Schaefer, Ethan I.
and Debary, Hiyam and Kumar, Ankur and Lal, Rohit and Dawson, Geoffrey
and Watson, Campbell and Dawson-Rigas, Rebekah I. and Maskey, Manil
and Bernab\'e-Moreno, Juan and Ramachandran, Rahul and Roy, Sujit},
title = {{SomBench}: Benchmark Dataset for Advancing Machine Learning in Lunar Science},
year = {2026},
howpublished = {\url{https://huggingface.co/collections/nasa-ibm-ai4science/lunar-fm-ml-ready-benchmark-dataset-sombench}}
}License
Released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.