3 papers
cs.CV2026
Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance
Amandeep Kaur, Mirali Purohit, Gedeon Muhawenayo +2
New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Performance differences are la…
cs.CV2026
MOMO: Mars Orbital Model Foundation Model for Mars Orbital Applications
Mirali Purohit, Bimal Gajera, Irish Mehta +8
We introduce MOMO, the first multi-sensor foundation model for Mars remote sensing. MOMO uses model merge to integrate representations learned independently from three key Martian…
cs.CV2025
Mars-Bench: A Benchmark for Evaluating Foundation Models for Mars Science Tasks
Mirali Purohit, Bimal Gajera, Vatsal Malaviya +6
Foundation models have enabled rapid progress across many specialized domains by leveraging large-scale pre-training on unlabeled data, demonstrating strong generalization to a var…