Publications by Devis Tuia | EPFL Graph Search

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Devis Tuia, Michael Lehning, Yael Frischholz

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

2025

MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps

Devis Tuia, Valentin Alexandre Guy Gabeff, Alexander Mathis

MammAlps is a multimodal and multi-view dataset of wildlife behavior monitoring. Nine camera-traps were placed at three monitoring sites in the Swiss National Park, from which we curated over 14 hours of video with audio, 2D segmentation maps and 8.5 hours ...

Zenodo2025

Urban Visual-spatial Intelligence: Linking Human and Sensor Perception for Sustainable Urban Development

Devis Tuia

Understanding urban perception enhances urban intelligence research, aiding sustainable development and smart city. Urban Visual-Spatial Intelligence (UVSI) integrates human and sensor perception. This Perspective explores UVSI's potential for sustainable ...

SPRINGERNATURE2025

On the added value of sequential deep learning for the upscaling of evapotranspiration

Devis Tuia

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

2025

Self-supervised Change Detection via Cooperative Learning: A Two-Player Model

Devis Tuia

Change detection (CD) is a critical task in remote sensing, especially for monitoring dynamic urban environments. In this work, we present a novel, self-supervised CD approach that leverages a two-player architecture to improve the reliability and accuracy ...

IEEE2025

Active learning and margin strategies for arrhythmia classification in implantable devices

Devis Tuia

Background and Objectives: The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new artificial intelligence algorithms are opening up new opportunities for electrophysiological knowledge ex ...

2025

Multi-Scale Grouped Prototypes for Interpretable Semantic Segmentation

Devis Tuia, Hugo Laurent Pascal Porta

Prototypical part learning is emerging as a promising approach for making semantic segmentation interpretable. The model selects real patches seen during training as prototypes and constructs the dense prediction map based on the similarity between parts o ...

IEEE2025

How to minimize the annotation effort in aerial wildlife surveys

Devis Tuia

Aircraft-based monitoring of wildlife is a popular way among conservation practitioners to obtain animal population counts over large areas. Nowadays, these aerial censuses are becoming increasingly scalable due to the advent of drone technology, which is ...

2025

Evaluating Language Biases in Remote Sensing Visual Question Answering: The role of spatial attributes, language diversity, and the need for clearer evaluation

Devis Tuia, Christel Marie Tartini-Chappuis, Vincent Alexandre Mendez

Remote sensing visual question answering (RSVQA) opens new opportunities for the use of Earth observation imagery by the general public, by enabling human-machine interaction with natural language. Building on the recent advances in natural language proces ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Preface: Advancing deep learning for remote sensing time series data analysis

Devis Tuia

This special issue explores the burgeoning field of deep learning for remote sensing time series analysis. The 20 contributed papers showcase diverse applications, including land cover mapping, change detection, atmospheric and biophysical/biochemical para ...

2025

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

2025

Semantic Segmentation of Benthic Classes in Reef Environments Using a Large Vision Transformer

Devis Tuia, Jonathan Paul Sauder

Coral reefs are crucial for biodiversity and provide vital resources for humankind. But despite such a central role, they are confronted to increasing threats linked to climate change, pollution, and local stressors. To ensure effective conservation, effic ...

Springer Science and Business Media Deutschland GmbH2025

MaskSDM: Adaptive Species Distribution Modeling Through Data Masking

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden

Species distribution models (SDMs) correlate species occurrences with environmental conditions and underpin much of ecological research. A key consideration in developing SDMs is selecting the optimal set of environmental predictor variables, which vary de ...

Springer Science and Business Media Deutschland GmbH2025

POLO – Point-Based, Multi-class Animal Detection

Devis Tuia

Automated wildlife surveys based on drone imagery and object detection technology are a powerful and increasingly popular tool in conservation biology. Most detectors require training images with annotated bounding boxes, which are tedious, expensive, and ...

Springer Science and Business Media Deutschland GmbH2025

Understanding the sentiment associated with cultural ecosystem services using images and text from social media

Devis Tuia

Social media is increasingly being employed to develop Cultural Ecosystem Services (CES) indicators. The image-sharing platform Flickr has been one of the most popular sources of data. Most large-scale studies, however, tend to only use the number of image ...

2024

Scalable semantic 3D mapping of coral reefs with deep learning

Devis Tuia, Anders Meibom, Jonathan Paul Sauder, Guilhem Maurice Louis Banc-Prandi

Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existen ...

2024

Regional uniqueness of tree species composition and response to forest loss and climate change

Devis Tuia, Nina Marion Aurélia Van Tiel, Loïc Pellissier

The conservation and restoration of forest ecosystems require detailed knowledge of the native plant compositions. Here, we map global forest tree composition and assess the impacts of historical forest cover loss and climate change on trees. The global oc ...

Nature Portfolio2024

Multi-Scale and Multimodal Species Distribution Modeling

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Benjamin Alexander Kellenberger

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (envir ...

Springer Nature Link2024

Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

Devis Tuia

Earth observation (EO) is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing ur ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Springernature2024