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SomBench

model2 events
papersTODAY 04:00 UTC

SomBench: Benchmark Dataset for Machine Learning in Lunar Science

Researchers introduced SomBench, a benchmark dataset aimed at machine learning applications in lunar science. The work addresses the difficulty of combining observations from multiple lunar orbital missions, which differ in sampling, projection, and instrument characteristics. It is intended to give researchers a common basis for evaluating models on lunar data.

benchmark/dataset/lunar-science/research
papersTODAY 04:00 UTC

Multimodal Foundation Model Pretrained for Lunar Remote Sensing

Researchers introduce a multimodal, multiresolution foundation model trained from scratch for lunar remote sensing. It was pretrained on SomBench, a geographically partitioned dataset of roughly two million co-registered tile bundles covering 11 sensor modalities at two spatial resolutions of 1 m/pixel. The work targets general-purpose representation learning for planetary surface analysis.

remote-sensing/multimodal/foundation-models/space