72 citations · 287 across the 33 of their papers we have counts for
36 papers
Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels
Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek +4
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of pattern recognition and classification tasks. In this work, we consider extendi…
Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images
Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek +4
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers. It allows to exchange highly…
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…
A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification
Amer Delilbasic, Bertrand Le Saux, Morris Riedel +2
In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in q…
Analysis of COVID-19 first wave in the US based on demographic, mobility, and environmental variables
Dario Spiller, Gabriele Santin, Alessandro Sebastianelli +6
COVID-19 had a strong and disruptive impact on our society, and yet further analyses on most relevant factors explaining the spread of the pandemic are needed. Interdisciplinary st…
Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines
Artur Miroszewski, Jakub Mielczarek, Grzegorz Czelusta +4
Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with q…
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…
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé +2
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic u…
Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé +2
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire m…
Rapid training of quantum recurrent neural networks
Michał Siemaszko, Adam Buraczewski, Bertrand Le Saux +1
Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness Recurrent Neural Networks (RNNs). However, while their predi…
Self-supervised learning -- A way to minimize time and effort for precision agriculture?
Michael L. Marszalek, Bertrand Le Saux, Pierre-Philippe Mathieu +2
Machine learning, satellites or local sensors are key factors for a sustainable and resource-saving optimisation of agriculture and proved its values for the management of agricult…
Beyond Ansätze: Learning Quantum Circuits as Unitary Operators
Bálint Máté, Bertrand Le Saux, Maxwell Henderson
This paper explores the advantages of optimizing quantum circuits on wires as operators in the unitary group . We run gradient-based optimization in the Lie algebra $\m…
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing
Gaston Lenczner, Adrien Chan-Hon-Tong, Bertrand Le Saux +2
We propose in this article to build up a collaboration between a deep neural network and a human in the loop to swiftly obtain accurate segmentation maps of remote sensing images.…
Weakly-supervised continual learning for class-incremental segmentation
Gaston Lenczner, Adrien Chan-Hon-Tong, Nicola Luminari +1
Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing. Starting from a neural network already trained for semantic…
Weakly Supervised Change Detection Using Guided Anisotropic Difusion
Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch +1
Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms. While these dataset…
How to find a good image-text embedding for remote sensing visual question answering?
Christel Chappuis, Sylvain Lobry, Benjamin Kellenberger +2
Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone. VQA considers a que…
On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Alessandro Sebastianelli, Daniela A. Zaidenberg, Dario Spiller +2
This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote s…
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport
Rémy Leroy, Pauline Trouvé-Peloux, Frédéric Champagnat +2
Good quality reconstruction and comprehension of a scene rely on 3D estimation methods. The 3D information was usually obtained from images by stereo-photogrammetry, but deep learn…
Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
Veda Sunkara, Matthew Purri, Bertrand Le Saux +1
To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground tr…
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch +2
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. Indeed, raw image data are abundant whi…
Interactive Learning for Semantic Segmentation in Earth Observation
Gaston Lenczner, Adrien Chan-Hon-Tong, Nicola Luminari +2
Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding. However, these maps are often partially inaccurate due to a v…
DISIR: Deep Image Segmentation with Interactive Refinement
Gaston Lenczner, Bertrand Le Saux, Nicola Luminari +2
This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annota…
Technical Report: Co-learning of geometry and semantics for online 3D mapping
Marcela Carvalho, Maxime Ferrera, Alexandre Boulch +3
This paper is a technical report about our submission for the ECCV 2018 3DRMS Workshop Challenge on Semantic 3D Reconstruction \cite{Tylecek2018rms}. In this paper, we address 3D s…
Distance transform regression for spatially-aware deep semantic segmentation
Nicolas Audebert, Alexandre Boulch, Bertrand Le Saux +1
Understanding visual scenes relies more and more on dense pixel-wise classification obtained via deep fully convolutional neural networks. However, due to the nature of the network…