Publications associées à Programme Landsat

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Gaston Jean Lenczner, Thiên-Anh Claris Nguyen, Marc Conrad Russwurm

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Devis Tuia, Marc Conrad Russwurm, Lloyd Haydn Hughes, Oleg Antropov, Sudipan Saha

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compare ...

The Institute of Electrical and Electronics Engineers, Inc2023

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Danya Li

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-av ...

Elsevier2023

Monitoring Mesoscale to Submesoscale Processes in Large Lakes with Sentinel-1 SAR Imagery: The Case of Lake Geneva

David Andrew Barry, Ulrich Lemmin, Seyed Mahmood Hamze Ziabari, Mehrshad Foroughan

As in oceans, large-scale coherent circulations such as gyres and eddies are ubiquitous features in large lakes that are subject to the Coriolis force. They play a crucial role in the horizontal and vertical distribution of biological, chemical and physica ...

2022

Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover

Devis Tuia, Marc Conrad Russwurm

Learning to predict accurately from a few data samples is a central challenge in modern data-hungry machine learning. On natural images, human vision typically outperforms deep learning approaches on few-shot learning. However, we hypothesize that aerial a ...

IEEE2022

Deep Learning‐based Semantic Segmentation in Remote Sensing

Devis Tuia

Semantic segmentation consists of the generation of a categorical map, given an image in which each pixel of the image is automatically assigned a class. Deep learning allows the influence of the pixel's context to be learned by capturing the non-linear re ...

Wiley2021

A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta

Giovanni De Cesare, Sameh Ahmad Kantoush

River bank (RB) erosion is a global issue aecting livelihoods and properties of millions of people. However, it has not received enough attention in the Vietnamese Mekong Delta (VMD), i.e., the world’s third largest delta, compared to salinity intrusion an ...

2020

Sun-synchronous repeat ground tracks and other useful orbits for future space missions

Sun Hee Kim, Luzius Gregor Kronig

The development of oceanography and meteorology has greatly benefited from satellite-based data of Earth's atmosphere and ocean. Traditional Earth observation missions have utilised Sun-synchronous orbits with repeat ground tracks due to their advantages i ...

2020

Resolving biogeochemical processes in lakes using remote sensing

Alfred Johny Wüest, Damien Bouffard, Vincent Maurice Nouchi, Theo Baracchini

Remote sensing helps foster our understanding of inland water processes allowing a synoptic view of water quality parameters. In the context of global monitoring of inland waters, we demonstrate the benefit of combining in-situ water analysis, hydrodynamic ...

2019

Recent advances with optical upconverters made from all-organic and hybrid materials

Roland Hany, Karen Verena Strassel

The growing interest in near-infrared (NIR) imaging is explained by the increasing number of applications in this spectral range, which includes process monitoring and medical imaging. NIR-to-visible optical upconverters made by integrating a NIR photosens ...

2019