Unité

Laboratoire de science computationnelle pour l'environnement et l'observation de la Terre

Laboratoire

Publications associées (63)

Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents

We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learn ...

Springer International Publishing2022

Loss landscape and symmetries in Neural Networks

Mario Geiger

Neural networks (NNs) have been very successful in a variety of tasks ranging from machine translation to image classification. Despite their success, the reasons for their performance are still not well-understood. This thesis explores two main themes: lo ...

EPFL2021

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Fei Mi

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery

Devis Tuia, Diego Marcos Gonzalez

While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change ...

Springer, Cham2021

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

Devis Tuia, Benjamin Alexander Kellenberger, Xiaochen Zheng, Rui Gong

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 ...

IEEE2021

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

On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI

Devis Tuia, Alex Hubertus Levering

The environment where we live and recreate can have a significant effect on our well-being. More beautiful landscapes have considerable benefits to both health and quality of life. When we chose where to live or our next holiday destination, we do so accor ...

2021

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

Devis Tuia, Sylvain Lobry

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 ...

IEEE2021

Cross-Modal Learning of Housing Quality in Amsterdam

Devis Tuia, Alex Hubertus Levering

In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts th ...

ACM2021

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines

Maksym Andriushchenko

Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empirical performance of fine-tuned models, fine-tuning is an unstable process: tr ...

2021

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