3 citations · 7 across the 10 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
Enhancing Maritime Situational Awareness through End-to-End Onboard Raw Data Analysis
Roberto Del Prete, Manuel Salvoldi, Domenico Barretta +5
Satellite-based onboard data processing is crucial for time-sensitive applications requiring timely and efficient rapid response. Advances in edge artificial intelligence are shift…
Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting
Brandon Victor, Mathilde Letard, Peter Naylor +4
Floods are among the most common and devastating natural hazards, imposing immense costs on our society and economy due to their disastrous consequences. Recent progress in weather…
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
Ziyang Zhang, Plamen Angelov, Dmitry Kangin +1
In this paper, we address two critical challenges in the domain of flood detection: the computational expense of large-scale time series change detection and the lack of interpreta…
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Vladimir Zaigrajew, Hubert Baniecki, Lukasz Tulczyjew +4
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for ide…
Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok +4
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can…
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
Bartosz Grabowski, Maciej Ziaja, Michal Kawulok +3
Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can…