arXiv Machine Learning June 19, 2026 By Jonathan Hecht, Lukas Arzoumanidis, Ziyue Li, Youness Dehbi

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

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

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arXiv Machine Learning

Jun 2

UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations

arXiv:2510. 13774v2 Announce Type: replace Abstract: Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data.

By Dominik J. M\"uhlematter, Lin Che, Ye Hong, Martin Raubal, Nina Wiedemann

llms multimodal benchmarks

arXiv Computer Vision

Sep 15

GeoMetric: Injecting Metric Geographic Structure into Worldwide Image Geo-Localization

arXiv:2606.08918v2 Announce Type: replace Abstract: Worldwide image geo-localization aims to determine where on Earth a single image was captured. However, visually similar scenes may lie thousands o...

By Junchao Cui, Xuanzi Ma, Wenqi Shi, Nan Wu, Biru Zhu, Xiangyang Luo

llms rag multimodal benchmarks

arXiv AI

Aug 10

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.

By Kevin Lane, Zhongying Wang, Esther Rolf, Morteza Karimzadeh

rag efficiency multimodal benchmarks

arXiv Computer Vision

Aug 27

GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction

GTPred is a new benchmark for geo‑temporal prediction that evaluates multi‑modal large language models (MLLMs) on 370 images taken across 120 years worldwide. It assesses predictions by matching both the year and a hierarchical location sequence, and includes annotated reasoning chains to test intermediate reasoning. Experiments on 15 MLLMs show that while visual perception is strong, models still lack world knowledge and geo‑temporal reasoning, and that adding temporal data improves location inference.

By Jinnao Li, Tingzhu Chen, Changbo Wang

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 25

What's in an Earth Embedding? An Explainability Analysis of Location Encoders

arXiv:2606. 24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network.

By Livia Betti, Sebastian Ricke, Ivica Obadic, Adam J. Stewart, Esther Rolf

rag

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