13 citations · 29 across the 16 of their papers we have counts for
11 papers · 1 filter
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…
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…
Scaling Laws for Geospatial Foundation Models: A case study on PhilEO Bench
Nikolaos Dionelis, Riccardo Musto, Jente Bosmans +7
Foundation Models (FMs) have achieved state-of-the-art performance across domains by leveraging large-scale pretraining. In Earth Observation (EO), the availability of petabyte-sca…
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
Nikolaos Dionelis, Jente Bosmans, Nicolas Longépé
Performing accurate confidence quantification and assessment in pixel-wise regression tasks, which are downstream applications of AI Foundation Models for Earth Observation (EO), i…
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…
Building Age Estimation: A New Multi-Modal Benchmark Dataset and Community Challenge
Nikolaos Dionelis, Alessandra Feliciotti, Mattia Marconcini +8
Estimating the construction year of buildings is critical for advancing sustainability, as older structures often lack energy-efficient features. Sustainable urban planning relies…
Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI
Nikolaos Dionelis, Casper Fibaek, Luke Camilleri +3
When we are primarily interested in solving several problems jointly with a given prescribed high performance accuracy for each target application, then Foundation Models should fo…
Improving EO Foundation Models with Confidence Assessment for enhanced Semantic segmentation
Nikolaos Dionelis, Nicolas Longepe
Confidence assessments of semantic segmentation algorithms are important. Ideally, deep learning models should have the ability to predict in advance whether their output is likely…
A Semantic Segmentation-guided Approach for Ground-to-Aerial Image Matching
Francesco Pro, Nikolaos Dionelis, Luca Maiano +2
Nowadays the accurate geo-localization of ground-view images has an important role across domains as diverse as journalism, forensics analysis, transports, and Earth Observation. T…
Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images
Nikolaos Dionelis, Francesco Pro, Luca Maiano +2
Data from satellites or aerial vehicles are most of the times unlabelled. Annotating such data accurately is difficult, requires expertise, and is costly in terms of time. Even if…
PhilEO Bench: Evaluating Geo-Spatial Foundation Models
Casper Fibaek, Luke Camilleri, Andreas Luyts +2
Massive amounts of unlabelled data are captured by Earth Observation (EO) satellites, with the Sentinel-2 constellation generating 1.6 TB of data daily. This makes Remote Sensing a…