10 citations · 19 across the 7 of their papers we have counts for
10 papers
Reuse or Relearn? A Spectral View of Earth Observation Foundation Models
Mehmet Ozgur Turkoglu, Valerio Marsocci, Dominik J. Mühlematter +3
Foundation models are rarely used as generic, frozen feature extractors; instead, they are fine-tuned for the target downstream application. This practice is particularly prevalent…
COP-GEN-Beta: Unified Generative Modelling of COPernicus Imagery Thumbnails
Miguel Espinosa, Valerio Marsocci, Yuru Jia +2
In remote sensing, multi-modal data from various sensors capturing the same scene offers rich opportunities, but learning a unified representation across these modalities remains a…
Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection
Romain Thoreau, Valerio Marsocci, Dawa Derksen
As large-scale heterogeneous data sets become increasingly available, adapting foundation models at low cost has become a key issue. Seminal works in natural language processing, e…
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?
Yuru Jia, Valerio Marsocci, Ziyang Gong +3
Self-supervised learning (SSL) has revolutionized representation learning in Remote Sensing (RS), advancing Geospatial Foundation Models (GFMs) to leverage vast unlabeled satellite…
PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
Valerio Marsocci, Yuru Jia, Georges Le Bellier +12
Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow.…
Cross-sensor self-supervised training and alignment for remote sensing
Valerio Marsocci, Nicolas Audebert
Large-scale ''foundation models'' have gained traction as a way to leverage the vast amounts of unlabeled remote sensing data collected every day. However, due to the multiplicity…
Conditional computation in neural networks: principles and research trends
Simone Scardapane, Alessandro Baiocchi, Alessio Devoto +3
This article summarizes principles and ideas from the emerging area of applying \textit{conditional computation} methods to the design of neural networks. In particular, we focus o…
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Valerio Marsocci, Nicolas Gonthier, Anatol Garioud +2
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (R…
Inferring 3D change detection from bitemporal optical images
Valerio Marsocci, Virginia Coletta, Roberta Ravanelli +2
Change detection is one of the most active research areas in Remote Sensing (RS). Most of the recently developed change detection methods are based on deep learning (DL) algorithms…
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation
Valerio Marsocci, Simone Scardapane
In the field of Earth Observation (EO), Continual Learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing th…