arXiv AI August 10, 2026 By Kevin Lane, Zhongying Wang, Esther Rolf, Morteza Karimzadeh

SLED: Scalable Location Encoding via Distillation

arXiv:2608. 06612v1 Announce Type: cross Abstract: The plethora of readily available geospatial data offers exciting opportunities to learn high quality representations of the planet, but the sheer size of the Earth Observations (EO), differing modalities, and different sensor types pose significant challenges in doing so.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 19

TerraMind: Large-Scale Generative Multimodality for Earth Observation

arXiv:2504. 11171v5 Announce Type: replace-cross Abstract: We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO).

By Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp, Rahul Ramachandran, Paolo Fraccaro, Thomas Brunschwiler, Gabriele Cavallaro, Juan Bernabe-Moreno, Nicolas Long\'ep\'e

fine-tuning multimodal benchmarks

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

Jun 19

Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying

arXiv:2606. 20167v1 Announce Type: new Abstract: Spatial prediction tasks are often limited by a lack of high-quality labelled ground-truth observations.

By Jonathan Hecht, Lukas Arzoumanidis, Ziyue Li, Youness Dehbi

rag multimodal

arXiv AI

Jul 7

Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing

arXiv:2607. 03644v1 Announce Type: cross Abstract: Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, radar, gravity, and elemental composition.

By Ayush Prasad, Swarnalee Mazumder

llms computer-vision multimodal benchmarks

arXiv AI

Jun 10

Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks

arXiv:2606. 10819v1 Announce Type: cross Abstract: RS-MLLMs enable natural-language understanding and spatial reasoning over earth observation imagery.

By Miaoxin Cai, Guanqun Wang, Wei Zhang, Guangyao Zhou, Yin Zhuang, Tong Zhang, Hao Wang, He Chen, Jun Li

llms multimodal benchmarks safety

Hugging Face Trending Papers

Jun 1

LALE: Lightweight-Transformer Architecture for Land-Cover Estimation

Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency.

llms computer-vision efficiency benchmarks

Related stories

TerraMind: Large-Scale Generative Multimodality for Earth Observation

Emerging Flexible Designs for Geospatial Multimodal Foundation Models

Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying

Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing

Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks

LALE: Lightweight-Transformer Architecture for Land-Cover Estimation