1 code implementation • 17 Apr 2024 • Nikolaos Dionelis, Francesco Pro, Luca Maiano, Irene Amerini, Bertrand Le Saux
In this paper, we develop a new model for semantic segmentation of unlabelled images, the Non-annotated Earth Observation Semantic Segmentation (NEOS) model.
2 code implementations • 17 Apr 2024 • Francesco Pro, Nikolaos Dionelis, Luca Maiano, Bertrand Le Saux, Irene Amerini
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.
2 code implementations • 22 Mar 2024 • Zhitong Xiong, Yi Wang, Fahong Zhang, Adam J. Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps-Valls, Xiao Xiang Zhu
The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data.
no code implementations • 29 Jan 2024 • Francesco Mauro, Alessandro Sebastianelli, Bertrand Le Saux, Paolo Gamba, Silvia Liberata Ullo
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 (HQGCNN), combining a Graph Convolutional Neural Network (GCNN) with a Quantum Multilayer Perceptron (MLP).
1 code implementation • 9 Jan 2024 • Casper Fibaek, Luke Camilleri, Andreas Luyts, Nikolaos Dionelis, Bertrand Le Saux
Massive amounts of unlabelled data are captured by Earth Observation (EO) satellites, with the Sentinel-2 constellation generating 1. 6 TB of data daily.
no code implementations • 28 Nov 2023 • Christel Chappuis, Eliot Walt, Vincent Mendez, Sylvain Lobry, Bertrand Le Saux, Devis Tuia
While new, improved and less-biased datasets appear as a necessity for the development of the promising field of RSVQA, we demonstrate that more informed, relative evaluation metrics remain much needed to transparently communicate results of future RSVQA methods.
no code implementations • 13 Nov 2023 • Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Stych
Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using multitemporal Sentinel-1 SAR and ancillary data.
no code implementations • 13 Nov 2023 • Artur Miroszewski, Jakub Nalepa, Bertrand Le Saux, Jakub Mielczarek
In this paper, we investigate the application of QML in the field of remote sensing.
no code implementations • 10 Nov 2023 • Fulvio Sanguigni, Mikolaj Czerkawski, Lorenzo Papa, Irene Amerini, Bertrand Le Saux
The advancements in the state of the art of generative Artificial Intelligence (AI) brought by diffusion models can be highly beneficial in novel contexts involving Earth observation data.
no code implementations • 5 Oct 2023 • Kai Jeggle, Mikolaj Czerkawski, Federico Serva, Bertrand Le Saux, David Neubauer, Ulrike Lohmann
Clouds containing ice particles play a crucial role in the climate system.
no code implementations • 3 Oct 2023 • Su Yeon Chang, Michele Grossi, Bertrand Le Saux, Sofia Vallecorsa
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 noises.
no code implementations • 14 Jul 2023 • Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek, Grzegorz Czelusta, Bartosz Grabowski, Bertrand Le Saux, Jakub Nalepa
In this work, we consider extending classic SVMs with quantum kernels and applying them to satellite data analysis.
no code implementations • 26 Jun 2023 • Artur Miroszewski, Jakub Mielczarek, Filip Szczepanek, Grzegorz Czelusta, Bartosz Grabowski, Bertrand Le Saux, Jakub Nalepa
The optimization of Kernel-Target Alignment (TA) has been recently proposed as a way to reduce the number of hardware resources in quantum classifiers.
no code implementations • 16 Jun 2023 • Bartosz Grabowski, Maciej Ziaja, Michal Kawulok, Piotr Bosowski, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa
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.
no code implementations • 15 May 2023 • Devis Tuia, Konrad Schindler, Begüm Demir, Gustau Camps-Valls, Xiao Xiang Zhu, Mrinalini Kochupillai, Sašo Džeroski, Jan N. van Rijn, Holger H. Hoos, Fabio Del Frate, Mihai Datcu, Jorge-Arnulfo Quiané-Ruiz, Volker Markl, Bertrand Le Saux, Rochelle Schneider
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.
no code implementations • 21 Mar 2023 • Amer Delilbasic, Bertrand Le Saux, Morris Riedel, Kristel Michielsen, Gabriele Cavallaro
Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective in them.
no code implementations • 16 Feb 2023 • Artur Miroszewski, Jakub Mielczarek, Grzegorz Czelusta, Filip Szczepanek, Bartosz Grabowski, Bertrand Le Saux, Jakub Nalepa
In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis.
no code implementations • 24 Oct 2022 • Bartosz Grabowski, Maciej Ziaja, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa
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.
no code implementations • 3 Aug 2022 • Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data.
no code implementations • 3 Aug 2022 • Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux, Jakub Nalepa
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment.
1 code implementation • 1 Jul 2022 • Michał Siemaszko, Adam Buraczewski, Bertrand Le Saux, Magdalena Stobińska
Here, we propose to extend the concept of RRNs by including continuous-variable quantum resources in it, and to use a quantum-enhanced RNN to overcome these obstacles.
1 code implementation • 5 Apr 2022 • Michael L. Marszalek, Bertrand Le Saux, Pierre-Philippe Mathieu, Artur Nowakowski, Daniel Springer
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 agricultural land.
no code implementations • 1 Mar 2022 • Bálint Máté, Bertrand Le Saux, Maxwell Henderson
This paper explores the advantages of optimizing quantum circuits on $N$ wires as operators in the unitary group $U(2^N)$.
1 code implementation • 4 Jan 2022 • Gaston Lenczner, Adrien Chan-Hon-Tong, Bertrand Le Saux, Nicola Luminari, Guy Le Besnerais
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.
1 code implementation • 4 Jan 2022 • Gaston Lenczner, Adrien Chan-Hon-Tong, Nicola Luminari, Bertrand Le Saux
Transfer learning is a powerful way to adapt existing deep learning models to new emerging use-cases in remote sensing.
no code implementations • 31 Dec 2021 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
Large scale datasets created from crowdsourced labels or openly available data have become crucial to provide training data for large scale learning algorithms.
no code implementations • 24 Sep 2021 • Christel Chappuis, Sylvain Lobry, Benjamin Kellenberger, Bertrand Le Saux, Devis Tuia
Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone.
1 code implementation • 20 Sep 2021 • Alessandro Sebastianelli, Daniela A. Zaidenberg, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo
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 sensing.
no code implementations • 30 Jul 2021 • Rémy Leroy, Pauline Trouvé-Peloux, Frédéric Champagnat, Bertrand Le Saux, Marcela Carvalho
The 3D information was usually obtained from images by stereo-photogrammetry, but deep learning has recently provided us with excellent results for monocular depth estimation.
no code implementations • ICLR Workshop EBM 2021 • Javiera Castillo Navarro, Bertrand Le Saux, Alexandre Boulch, Sébastien Lefèvre
The large amount of data, available thanks to the recent sensors, have made possible the use of deep learning for Earth Observation.
1 code implementation • 26 Jan 2021 • Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms.
no code implementations • 5 Nov 2020 • Veda Sunkara, Matthew Purri, Bertrand Le Saux, Jennifer Adams
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 truth data into the segmentation of satellite imagery of floods.
no code implementations • 15 Oct 2020 • Javiera Castillo-Navarro, Bertrand Le Saux, Alexandre Boulch, Nicolas Audebert, Sébastien Lefèvre
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms.
1 code implementation • 23 Sep 2020 • Gaston Lenczner, Adrien Chan-Hon-Tong, Nicola Luminari, Bertrand Le Saux, Guy Le Besnerais
Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding.
1 code implementation • 31 Mar 2020 • Gaston Lenczner, Bertrand Le Saux, Nicola Luminari, Adrien Chan Hon Tong, Guy Le Besnerais
Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations.
1 code implementation • 18 Nov 2019 • Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Frédéric Champagnat, Andrés Almansa
Aerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models.
no code implementations • 4 Nov 2019 • Marcela Carvalho, Maxime Ferrera, Alexandre Boulch, Julien Moras, Bertrand Le Saux, Pauline Trouvé-Peloux
This paper is a technical report about our submission for the ECCV 2018 3DRMS Workshop Challenge on Semantic 3D Reconstruction \cite{Tylecek2018rms}.
no code implementations • 4 Sep 2019 • Nicolas Audebert, Alexandre Boulch, Bertrand Le Saux, Sébastien Lefèvre
This work introduces a new semantic segmentation regularization based on the regression of a distance transform.
no code implementations • 17 Apr 2019 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms.
no code implementations • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection.
1 code implementation • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau
The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate.
4 code implementations • 19 Oct 2018 • Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch
This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images.
Ranked #1 on Change Detection on OSCD - 13ch
Change Detection Change detection for remote sensing images +1
1 code implementation • 5 Sep 2018 • Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat
We also investigate the influence of blur on depth prediction observing model uncertainty with a Bayesian neural network approach.
no code implementations • 7 Jun 2018 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
This work addresses the scarcity of annotated hyperspectral data required to train deep neural networks.
1 code implementation • 23 Nov 2017 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data.
no code implementations • 17 May 2017 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images.
no code implementations • 20 Jan 2017 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture.
no code implementations • 22 Sep 2016 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images.
no code implementations • 22 Sep 2016 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this work, we show how to use such deep networks to detect, segment and classify different varieties of wheeled vehicles in aerial images from the ISPRS Potsdam dataset.
no code implementations • 22 Sep 2016 • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework.