Publications by Devis Tuia | EPFL Graph Search

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This paper introduces TACOSS a text-image alignment approach that allows explainable land cover semantic segmentation by directly integrating semantic concepts encoded from texts. TACOSS combines convolutional neural networks for visual feature extraction ...

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 ...

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multisensor, and multiangular images is available. In these situations, images sh ...

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 ...

Asking questions about visual environments is a crucial way for intelligent agents to understand rich multi-faceted scenes, raising the importance of Visual Question Generation (VQG) systems. Apart from being grounded to the image, existing VQG systems can ...

Recently, generalist Vision Language Models (VLMs) have shown exceptional progress in tasks previously dominated by specialized computer vision models. This becomes more prevalent when visual grounding capabilities, such as the ability to reason over input ...

The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statist ...

Including spatial information is a key step for successful remote sensing image classification. In particular, when dealing with high spatial resolution, if local variability is strongly reduced by spatial filtering, the classification performance results ...

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 ...

In late December 1973, the United States enacted what some would come to call “the pitbull of environmental laws.” In the 50 years since, the formidable regulatory teeth of the Endangered Species Act (ESA) have been credited with considerable successes, ob ...

In some areas of Antarctica, blue-colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi-sensor remote sensing data (MODIS, RADARSAT-2, ...

Remote sensing visual question answering (RSVQA) aims at predicting an answer to a question (both in natural language) about an overhead image. Through natural language processing, this task allows end users to extract high-level information from remote se ...

Remote sensing visual question answering (RQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in ...

We address the task of automatically detecting and counting seabirds in unmanned aerial vehicle (UAV) imagery using deep convolutional neural networks (CNNs). Our study area, the coast of West Africa, harbours significant breeding colonies of terns and gul ...

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 ...

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 ...

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks ...

In this paper, we study how to extract visual concepts to understand landscape scenicness. Using visual feature representations from a Convolutional Neural Network (CNN), we learn a number of Concept Activation Vectors (CAV) aligned with semantic concepts ...

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 ...

Devis Tuia

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Text as a Richer Source of Supervision in Semantic Segmentation Tasks

Meta-learning to address diverse Earth observation problems across resolutions

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Semisupervised Manifold Alignment of Multimodal Remote Sensing Images

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

ConVQG: Contrastive Visual Question Generation with Multimodal Guidance

Training Visual Language Models with Object Detection: Grounded Change Descriptions in Satellite Images

Hydrometeor classification from two-dimensional video disdrometer data

Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM

Multi-Scale and Multimodal Species Distribution Modeling

A landmark environmental law looks ahead

Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica

Visual question answering on remote sensing images

Prompt–RSVQA: Prompting visual context to a language model for Remote Sensing Visual Question Answering

21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning

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

Active learning and margin strategies for arrhythmia classification in implantable devices

Detecting animals in African Savanna with UAVs and the crowds

Concept Discovery for The Interpretation of Landscape Scenicness

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