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Xiao Xiang Zhu
100 publications
Konrad Schindler
85 publications
Gustau Camps-Valls
61 publications
Devis Tuia
51 publications
Begüm Demir
39 publications
Sébastien Lefèvre
35 publications
Sašo Džeroski
28 publications
Alexandre Boulch
25 publications
Mihai Datcu
23 publications
Volker Markl
21 publications
Jakub Nalepa
20 publications

research

∙ 07/14/2023

Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels

Support vector machines (SVMs) are a well-established classifier effecti...

0 Artur Miroszewski, et al. ∙

research

∙ 06/26/2023

Optimizing Kernel-Target Alignment for cloud detection in multispectral satellite images

The optimization of Kernel-Target Alignment (TA) has been recently propo...

0 Artur Miroszewski, et al. ∙

research

∙ 06/16/2023

Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection

Cloud detection is a pivotal satellite image pre-processing step that ca...

0 Bartosz Grabowski, et al. ∙

research

∙ 05/15/2023

Artificial intelligence to advance Earth observation: a perspective

Earth observation (EO) is a prime instrument for monitoring land and oce...

0 Devis Tuia, et al. ∙

research

∙ 03/21/2023

A Single-Step Multiclass SVM based on Quantum Annealing for Remote Sensing Data Classification

In recent years, the development of quantum annealers has enabled experi...

5 Amer Delilbasic, et al. ∙

research

∙ 02/16/2023

Detecting Clouds in Multispectral Satellite Images Using Quantum-Kernel Support Vector Machines

Support vector machines (SVMs) are a well-established classifier effecti...

10 Artur Miroszewski, et al. ∙

research

∙ 10/24/2022

Self-Configuring nnU-Nets Detect Clouds in Satellite Images

Cloud detection is a pivotal satellite image pre-processing step that ca...

0 Bartosz Grabowski, et al. ∙

research

∙ 08/03/2022

A Multibranch Convolutional Neural Network for Hyperspectral Unmixing

Hyperspectral unmixing remains one of the most challenging tasks in the ...

11 Lukasz Tulczyjew, et al. ∙

research

∙ 08/03/2022

Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series

Maintaining farm sustainability through optimizing the agricultural mana...

0 Lukasz Tulczyjew, et al. ∙

research

∙ 07/01/2022

Rapid training of quantum recurrent neural network

Time series prediction is the crucial task for many human activities e.g...

19 Michał Siemaszko, et al. ∙

research

∙ 04/05/2022

Self-supervised learning – A way to minimize time and effort for precision agriculture?

Machine learning, satellites or local sensors are key factors for a sust...

0 Michael L. Marszalek, et al. ∙

research

∙ 03/01/2022

Beyond Ansätze: Learning Quantum Circuits as Unitary Operators

This paper explores the advantages of optimizing quantum circuits on N w...

0 Bálint Máté, et al. ∙

research

∙ 01/04/2022

DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing

We propose in this article to build up a collaboration between a deep ne...

45 Gaston Lenczner, et al. ∙

research

∙ 01/04/2022

Weakly-supervised continual learning for class-incremental segmentation

Transfer learning is a powerful way to adapt existing deep learning mode...

0 Gaston Lenczner, et al. ∙

research

∙ 12/31/2021

Weakly Supervised Change Detection Using Guided Anisotropic Difusion

Large scale datasets created from crowdsourced labels or openly availabl...

5 Rodrigo Caye Daudt, et al. ∙

research

∙ 09/24/2021

How to find a good image-text embedding for remote sensing visual question answering?

Visual question answering (VQA) has recently been introduced to remote s...

0 Christel Chappuis, et al. ∙

research

∙ 09/20/2021

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

This article aims to investigate how circuit-based hybrid Quantum Convol...

0 Alessandro Sebastianelli, et al. ∙

research

∙ 07/30/2021

Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport

Good quality reconstruction and comprehension of a scene rely on 3D esti...

11 Rémy Leroy, et al. ∙

research

∙ 10/15/2020

Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study

The development of semi-supervised learning techniques is essential to e...

0 Javiera Castillo-Navarro, et al. ∙

research

∙ 09/23/2020

Interactive Learning for Semantic Segmentation in Earth Observation

Dense pixel-wise classification maps output by deep neural networks are ...

0 Gaston Lenczner, et al. ∙

research

∙ 03/31/2020

DISIR: Deep Image Segmentation with Interactive Refinement

This paper presents an interactive approach for multi-class segmentation...

0 Gaston Lenczner, et al. ∙

research

∙ 11/18/2019

Multi-Task Learning of Height and Semantics from Aerial Images

Aerial or satellite imagery is a great source for land surface analysis,...

16 Marcela Carvalho, et al. ∙

research

∙ 11/04/2019

Technical Report: Co-learning of geometry and semantics for online 3D mapping

This paper is a technical report about our submission for the ECCV 2018 ...

19 Marcela Carvalho, et al. ∙

research

∙ 09/04/2019

Distance transform regression for spatially-aware deep semantic segmentation

Understanding visual scenes relies more and more on dense pixel-wise cla...

16 Nicolas Audebert, et al. ∙

research

∙ 04/17/2019

Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection

Large scale datasets created from user labels or openly available data h...

0 Rodrigo Caye Daudt, et al. ∙

research

∙ 10/19/2018

Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks

The Copernicus Sentinel-2 program now provides multispectral images at a...

0 Rodrigo Caye Daudt, et al. ∙

research

∙ 10/19/2018

Fully Convolutional Siamese Networks for Change Detection

This paper presents three fully convolutional neural network architectur...

0 Rodrigo Caye Daudt, et al. ∙

research

∙ 10/19/2018

High Resolution Semantic Change Detection

Change detection is one of the main problems in remote sensing, and is e...

4 Rodrigo Caye Daudt, et al. ∙

research

∙ 09/05/2018

Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?

Depth estimation is of critical interest for scene understanding and acc...

0 Marcela Carvalho, et al. ∙

research

∙ 06/07/2018

Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples

This work addresses the scarcity of annotated hyperspectral data require...

0 Nicolas Audebert, et al. ∙

research

∙ 11/23/2017

Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks

In this work, we investigate various methods to deal with semantic label...

0 Nicolas Audebert, et al. ∙

research

∙ 05/17/2017

Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

In this work, we investigate the use of OpenStreetMap data for semantic ...

0 Nicolas Audebert, et al. ∙

research

∙ 01/20/2017

Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)

In this work, we present a novel module to perform fusion of heterogeneo...

0 Nicolas Audebert, et al. ∙

research

∙ 09/22/2016

How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?

In this paper, we investigate the impact of segmentation algorithms as a...

0 Nicolas Audebert, et al. ∙

research

∙ 09/22/2016

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

This work investigates the use of deep fully convolutional neural networ...

0 Nicolas Audebert, et al. ∙

research

∙ 09/22/2016

On the usability of deep networks for object-based image analysis

As computer vision before, remote sensing has been radically changed by ...

0 Nicolas Audebert, et al. ∙

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