Hugging Face Trending Papers July 6, 2026

Probing Geospatial SSL Representations with Environmental Signals

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Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task. Most geospatial benchmarks evaluate representations solely through downstream tasks, providing limited insight into the information encoded within the representation itself.

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benchmarks

arXiv Machine Learning

Jul 7

Probing Geospatial SSL Representations with Environmental Signals

arXiv:2607. 05207v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task.

By Rohita Mocharla, Vishal M. Patel

benchmarks

arXiv Machine Learning

Aug 21

Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolation

arXiv:2608. 19766v1 Announce Type: cross Abstract: Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and visual structure.

By Daniele Rege Cambrin, Francesco Rossi, Mattia Varile

rag benchmarks

arXiv Machine Learning

Aug 17

Catching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature Permutations

arXiv:2608. 14372v1 Announce Type: new Abstract: Scientific data often describe entities whose features are jointly governed by the laws of physics, yet existing self-supervised learning (SSL) objectives largely ignore this physical coherence.

By Aleksei Rozanov, Arvind Renganathan, Vipin Kumar

arXiv AI

Jun 12

Emerging Flexible Designs for Geospatial Multimodal Foundation Models

arXiv:2606. 12595v1 Announce Type: cross Abstract: Foundation models are rapidly transforming Earth observation by enabling scalable pretraining across diverse unlabeled geospatial modalities.

By Philipe Dias, Waqwoya Abebe, Abhishek Potnis, Aristeidis Tsaris, Dan Lu, Xiao Wang, Dalton Lunga

computer-vision multimodal benchmarks safety

arXiv Machine Learning

Jul 10

Scalable and Trustworthy Earth Observation Foundation Models

arXiv:2607. 07758v1 Announce Type: new Abstract: Foundation models (FMs) have transformed machine learning from isolated task-specific model development toward general-purpose models pretrained on broad data and adapted to multiple downstream tasks.

By Syed Usama Imtiaz, Mitra Nasr Azadani, Nasrin Alamdari

reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Aug 6

Above-ground Biomass Estimation with Geospatial Foundation Models

arXiv:2608. 04792v1 Announce Type: new Abstract: Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale.

By Ghjulia Sialellia, Linus Scheibenreif, Jan Dirk Wegner, Konrad Schindler

rag computer-vision benchmarks

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Probing Geospatial SSL Representations with Environmental Signals

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