47 citations · 79 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
ConeQuest: A Benchmark for Cone Segmentation on Mars
Mirali Purohit, Jacob Adler, Hannah Kerner
Over the years, space scientists have collected terabytes of Mars data from satellites and rovers. One important set of features identified in Mars orbital images is pitted cones,…
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Gabriel Tseng, Ruben Cartuyvels, Ivan Zvonkov +3
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-superv…