Publication

Remote Sensing meets Deep Learning: Exploiting Spatio-Temporal-Spectral Satellite Images for Early Wildfire Detection

Related publications (29)

Why is the winner the best?

Gabriel Girard, Jian Wang, Ho Ling Li, Adrien Raphaël Depeursinge, Yong Yang, Fan Xia, Xiao Wang, Jing Li, Hui Wang

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Ieee Computer Soc2023

Lightning Nowcasting Using Solely Lightning Data

Farhad Rachidi-Haeri, Marcos Rubinstein, Amirhossein Mostajabi

Lightning is directly or indirectly responsible for significant human casualties and property damage worldwide. A timely prediction of its occurrence can enable authorities and the public to take necessary precautionary actions resulting in diminishing the ...

MDPI2023

Transformer Models for Vision

Jean-Baptiste Francis Marie Juliette Cordonnier

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

EPFL2023

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Jiancheng Yang, Stanislav Lukyanenko

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...

IEEE2023

Two-level Data Augmentation for Calibrated Multi-view Detection

Pascal Fua, Martin Pierre Engilberge, Zhiye Wang, Haixin Shi

Data augmentation has proven its usefulness to improve model generalization and performance. While it is commonly applied in computer vision application when it comes to multi-view systems, it is rarely used. Indeed geometric data augmentation can break th ...

IEEE COMPUTER SOC2023

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Christian Robert Abbet

Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...

EPFL2023

Multi-site, Multi-domain Airway Tree Modeling

Jiancheng Yang, Yi Wu, Ying Zhu, Boyu Zhang

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolut ...

Elsevier2023

Ionospheric Variations Induced by Thunderstorms in the Central Region of Argentina during the RELAMPAGO–CACTI Campaign

Farhad Rachidi-Haeri, Marcos Rubinstein

The ionosphere can be perturbed by solar and geomagnetic activity, earthquakes, thunderstorms, etc. In particular, electromagnetic pulses produced by thunderstorms can generate wave structures in the ionospheric plasma, which are known as atmospheric gravi ...

2023

The imprint of galaxy mergers on satellite planes in a cosmological context

Oliver Müller

Flattened and kinematically correlated planes of dwarf satellite galaxies have been observed in the Local Volume. The slinging out of satellites during host galaxy mergers has been suggested as a formation mechanism for these peculiar structures. We statis ...

2023

Neural Distributed Image Compression with Cross-Attention Feature Alignment

Ali Garjani

We consider the problem of compressing an information source when a correlated one is available as side information only at the decoder side, which is a special case of the distributed source coding problem in information theory. In particular, we consider ...

IEEE COMPUTER SOC2023