activity
20222026
most citedPrithvi WxC: Foundation Model for Weather and Climate

9 citations · 10 across the 11 of their papers we have counts for

collaborators
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7 papers · 1 filter

cs.CV2026

Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing

Paolo Fraccaro, Gabby Nyirjesy, Daniela Szwarcman +19

We present a multimodal foundation model for lunar remote sensing, pretrained from scratch on SomBench, a geographically partitioned corpus of nearly two million co-registered tile…

cs.CV2026

SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science

Himanshu Patil, Gabby Nyirjesy, Rachel A. Slank +19

Lunar orbital missions, such as Lunar Reconnaissance Orbiter, Kaguya/SELENE, Gravity Recovery and Interior Laboratory, and Lunar Prospector, among others, provide rich multi-instru…

cs.CV2025

GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI

Naomi Simumba, Nils Lehmann, Paolo Fraccaro +9

Geospatial Foundation Models (GeoFMs) are transforming Earth Observation (EO), but evaluation lacks standardized protocols. GEO-Bench-2 addresses this with a comprehensive framewor…

cs.CV2025

Quantizing Space and Time: Fusing Time Series and Images for Earth Observation

Gianfranco Basile, Johannes Jakubik, Benedikt Blumenstiel +2

We propose a task-agnostic framework for multimodal fusion of time series and single timestamp images, enabling cross-modal generation and robust downstream performance. Our approa…

cs.CV2025

TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data

Benedikt Blumenstiel, Paolo Fraccaro, Valerio Marsocci +8

Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public da…

cs.CV2025

TerraMind: Large-Scale Generative Multimodality for Earth Observation

Johannes Jakubik, Felix Yang, Benedikt Blumenstiel +13

We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale…

cs.CV2022★ 1 cited

Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data

Kevin Mayer, Lukas Haas, Tianyuan Huang +3

Current methods to determine the energy efficiency of buildings require on-site visits of certified energy auditors which makes the process slow, costly, and geographically incompl…