687 citations · 850 across the 15 of their papers we have counts for
15 papers
Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks
Alessandro Sebastianelli, Francesco Mauro, Giulia Ciabatti +4
A significant amount of remotely sensed data is generated daily by many Earth observation (EO) spaceborne and airborne sensors over different countries of our planet. Different app…
In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods
Artur Miroszewski, Marco Fellous Asiani, Jakub Mielczarek +2
Quantum Machine Learning (QML) has gathered significant attention through approaches like Quantum Kernel Machines. While these methods hold considerable promise, their quantum natu…
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…
Latent Style-based Quantum GAN for high-quality Image Generation
Su Yeon Chang, Supanut Thanasilp, Bertrand Le Saux +2
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images…
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…
A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction
Francesco Mauro, Alessandro Sebastianelli, Bertrand Le Saux +2
This paper explores an innovative fusion of Quantum Computing (QC) and Artificial Intelligence (AI) through the development of a Hybrid Quantum Graph Convolutional Neural Network (…
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…
Super-resolved rainfall prediction with physics-aware deep learning
S. Moran, B. Demir, F. Serva +1
Rainfall prediction at the kilometre-scale up to a few hours in the future is key for planning and safety. But it is challenging given the complex influence of climate change on cl…
Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data
J. F. Amieva, A. Austoni, M. A. Brovelli +4
Change detection (CD) methods have been applied to optical data for decades, while the use of hyperspectral data with a fine spectral resolution has been rarely explored. CD is app…
IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision
Kai Jeggle, Mikolaj Czerkawski, Federico Serva +3
Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this w…
Approximately Equivariant Quantum Neural Network for Group Symmetries in Images
Su Yeon Chang, Michele Grossi, Bertrand Le Saux +1
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of n…
Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward
Devis Tuia, Konrad Schindler, Begüm Demir +11
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's…
Multi-Task Learning of Height and Semantics from Aerial Images
Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux +2
Aerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models. In this investigation, we present a neural network fra…
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
Nicolas Audebert, Bertrand Saux, Sébastien Lefèvre
In recent years, deep learning techniques revolutionized the way remote sensing data are processed. Classification of hyperspectral data is no exception to the rule, but has intrin…