Publications related to Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles

Related publications (30)

Enabling Uncertainty Estimation in Iterative Neural Networks

Pascal Fua, Minh Hieu Lê, Doruk Oner, Nikita Durasov

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Curran Associates2024

Aggregating Spatial and Photometric Context for Photometric Stereo

David Honzátko

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

EPFL2024

Epidemicity indices and reproduction numbers from infectious disease data in connected human populations

Andrea Rinaldo, Lorenzo Mari, Cristiano Trevisin

We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to th ...

2024

Stay close, but not too close: aerial image analysis reveals patterns of social distancing in seal colonies

Devis Tuia, Benjamin Alexander Kellenberger

Many species aggregate in dense colonies. Species-specific spatial patterns provide clues about how colonies are shaped by various (a)biotic factors, including predation, temperature regulation or disease transmission. Using aerial imagery, we examined the ...

2023

Time Series Analysis of Urban Liveability

Devis Tuia, Diego Marcos Gonzalez

In this paper we explore deep learning models to monitor longitudinal liveability changes in Dutch cities at the neighbourhood level. Our liveability reference data is defined by a country-wise yearly survey based on a set of indicators combined into a liv ...

IEEE2023

Rigidity-Aware Detection for 6D Object Pose Estimation

Mathieu Salzmann, Yinlin Hu

Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor ...

Ieee Computer Soc2023

Probabilistic methods for neural combinatorial optimization

Nikolaos Karalias

The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

EPFL2023

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Rita Drissi Daoudi

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

EPFL2023

DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

Martin Jaggi, Mary-Anne Hartley, Tatjana Chavdarova, Jonathan Dönz

The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpa ...

NATURE PORTFOLIO2023

How deep convolutional neural networks lose spatial information with training

Matthieu Wyart, Leonardo Petrini, Francesco Cagnetta, Umberto Maria Tomasini

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image da ...

Iop Publishing Ltd2023

Estimating and Improving the Robustness of Attributions in Text

Ádám Dániel Ivánkay

End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...

EPFL2023

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Rongrong Li

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

ELSEVIER2023

Beyond undulation! Body morphology and sensing components of elongated animals and robots reveal skills to maintain competent locomotion

Laura Isabel Paez Coy

Locomotion is an essential evolutive innovation of living beings that allows them to colonize and dominate the planet. As diverse as animal morphologies are (living) and were (extinct), their locomotion modalities are also diverse. In particular, animal mo ...

EPFL2023

Improving the Training of Compact Neural Networks for Visual Recognition

Shuxuan Guo

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

EPFL2022

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Barbara Bruno, Jauwairia Nasir

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...

2022

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars

Giovanni De Cesare, Christian Mörtl

In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...

International Association for Hydro-Environment Engineering and Research (IAHR)2022

Semantic Segmentation of Remote Sensing Images With Sparse Annotations

Devis Tuia

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 ...

2022

Self-supervised Segmentation via Background Inpainting

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin, Isinsu Katircioglu, Victor Constantin

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained on. To address this when annotating data is prohibitively e ...

2022

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

Giancarlo Ferrari Trecate, Luca Furieri, Muhammad Zakwan, Clara Lucía Galimberti

Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...

2022

Vision-based Flocking in Aerial Robot Swarms

Fabian Maximilian Schilling

Aerial robot swarms have the potential to perform time-critical and dangerous tasks such as disaster response without compromising human safety. However, their reliance on external infrastructure such as global positioning for localization and wireless net ...

EPFL2022