Publications de Devis Tuia | EPFL Graph Search

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained ...

This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of ma ...

Ecological surveys increasingly rely on large‐scale image datasets, typically terabytes of imagery for a single survey. The ability to collect this volume of data allows surveys of unprecedented scale, at the cost of expansive volumes of photo‐interpretati ...

Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Com ...

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

Training convolutional neural networks (CNNs) for very high-resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor-intensive and time-consuming to produce. Moreover, professional photograph interpreter ...

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. Wh ...

Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, whi ...

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 ...

The technological evolution of optical sensors over the last few decades has provided remote sensing analysts with rich spatial, spectral, and temporal information. In particular, the increase in spectral resolution of hyperspectral images (HSIs) and infra ...

Explainable machine learning and uncertainty quantification have emerged as promising approaches to check the suitability and understand the decision process of a data-driven model, to learn new insights from data, but also to get more information about th ...

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Very often, the only reliable information available to perform change detection is the description of some unchanged regions. Since sometimes these regions do not contain all the relevant information to identify their counterpart (the changes), we consider ...

Methods based on Contrastive Language-Image Pre-training (CLIP) are nowadays extensively used in support of vision-and-language tasks involving remote sensing data, such as cross-modal retrieval. The adaptation of CLIP to this specific domain has relied on ...

In order to provide urban residents with suitable living conditions, it is essential to keep track of the liveability of neighbourhoods. This is traditionally done through surveys and by predictive modelling. However, surveying on a large scale is expensiv ...

In the last few years, active learning has been gaining growing interest in the remote sensing community in optimizing the process of training sample collection for supervised image classification. Current strategies formulate the active learning problem i ...

Image-based retrieval in large Earth observation archives is difficult, because one needs to navigate across thousands of candidate matches only with the proposition image as a guide. By using text as a query language, the retrieval system gains in usabili ...

Convolutional neural networks (CNNs) represent the new reference approach for semantic segmentation of very-high-resolution (VHR) images, due to their ability to automatically capture semantic information while learning relevant features. However, as for m ...

Plastic litter is a major environmental hazard that endangers human, animal, and plant health on the planet. A substantial portion of plastic pollutants is washed from rivers and beaches into the oceans and aggregates at the surface as marine debris before ...

Devis Tuia

Chattez avec Graph Search

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

Remote sensing image segmentation by active queries

Principal Polynomial Analysis

AIDE: Accelerating image‐based ecological surveys with interactive machine learning

Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Semantic Segmentation of Remote Sensing Images With Sparse Annotations

A deep learning framework for matching of SAR and optical imagery

Counting using deep learning regression gives value to ecological surveys

How to minimize the annotation effort in aerial wildlife surveys

Advances in hyperspectral image classification

A Whale's Tail - Finding the Right Whale in an Uncertain World

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Semi-Supervised Novelty Detection using SVM entire solution path

Multilingual Vision-Language Pre-training for the Remote Sensing Domain

Predicting the liveability of Dutch cities with aerial images and semantic intermediate concepts

SVM Active Learning Approach for Image Classification Using Spatial Information

Knowledge-Aware Cross-Modal Text-Image Retrieval for Remote Sensing Images

A Semisupervised CRF Model for CNN-Based Semantic Segmentation With Sparse Ground Truth

Improved marine debris detection in satellite imagery with automatic refinement of coarse hand annotations