Publications de Devis Tuia | EPFL Graph Search

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Visual Question Answering (VQA) on remote sensing imagery can help non-expert users in extracting information from Earth observation data. Current approaches follow a neural encoder-decoder design, combining convolutional and recurrent encoders together wi ...

Visual Question Answering is a new task that can facilitate the extraction of information from images through textual queries: it aims at answering an open-ended question formulated in natural language about a given image. In this work, we introduce a new ...

Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...

Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating anim ...

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 ...

Detecting and quantifying marine pollution and macroplastics is an increasingly pressing ecological issue that directly impacts ecology and human health. Here, remote sensing can provide reliable estimates of plastic pollution by regularly monitoring and d ...

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in datascarce regions. To address it we have develop ...

In this work, we propose an approach to aid in mapping small settlements, which are often misclassified by models trained on a large-scale context (global or regional). We leverage pre-trained land cover models and few-shot learning to enhance the detectio ...

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 ...

Introduced to enable a wider use of Earth Observation images using natural language, Remote Sensing Visual Question Answering (RSVQA) remains a challenging task, in particular for questions related to counting. To address this specific challenge, we propos ...

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, mor ...

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 ...

In this paper, we propose a strategy for fusing clustering maps obtained with different remote sensing sources. Dempster- Shafer (DS) Theory is a powerful fusion method that allows to combine classifications from different sources and handles ignorance, im ...

Devis Tuia

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Remote sensing visual question answering with a self-attention multi-modal encoder

RSVQA Meets Bigearthnet: A New, Large-Scale, Visual Question Answering Dataset for Remote Sensing

Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models

Meteor: Meta-learning connecting earth problems observed from space

Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images

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

Large-scale detection of marine debris in coastal areas with Sentinel-2

High-resolution population maps derived from Sentinel-1 and Sentinel-2

Detection of Settlements in Tanzania and Mozambique by Many Regional Few-Shot Models

Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current Approaches, Open Challenges, and Future Opportunities

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

Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering

Multi-task prompt-RSVQA to explicitly count objects on aerial images

Muti-modal learning in photogrammetry and remote sensing

Fine-grained population mapping from coarse census counts and open geodata

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

Mapping forest cover dynamics in the Swiss Alps using 70 years of aerial imagery

Multisource Clustering of Remote Sensing Images With Entropy-Based Dempster-Shafer Fusion

Foreword to the Special Issue on Machine Learning for Remote Sensing Data Processing

2016 IEEE GRSS Data Fusion Contest Very high temporal resolution from space