74 citations
- T. Corpetti4 · h 27
- R. Tavenard2 · h 19
- S. Lefèvre2 · h 38
- Arthur Le Guennec1 · h 2
- Baptiste Feldmann1 · h 3
- C. Fleurant1 · h 10
- Chair of Data Science in Earth Observation - Munich - Germany1 · h 1
- Charlotte Pelletier1 · h 16
- Chloé Friguet1 · h 9
- D. Amitrano1 · h 27
- Daniel Girardeau-Montaut1 · h 1
- D. Cordier1 · h 21
- Institut de Recherche en Informatique et Systèmes AléatoiresFR5 papers
- Université de Bretagne SudFR5 papers
- Université de RennesFR4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Université Rennes 2FR3 papers
- Institut Universitaire Européen de la MerFR2 papers
- Magellium (France)FR2 papers
- Association pour l'Utilisation du Rein Artificiel dans la région LyonnaiseFR1 paper
- Centre National d'Études SpatialesFR1 paper
- École Normale Supérieure de LyonFR1 paper
- European Space Astronomy CentreES1 paper
- Geomechanica (Canada)CA1 paper
8 papers
Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
Wassim Swaileh, Florent Imbert, Yann Soullard +2
Handwriting with digital pens is a common way to facilitate human-computer interaction through the use of Online Handwriting (OH) trajectory reconstruction. In this work, we focus…
3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data
Mathilde Letard, Dimitri Lague, Arthur Le Guennec +5
Three-dimensional data have become increasingly present in earth observation over the last decades. However, many 3D surveys are still underexploited due to the lack of accessible…
Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
François Painblanc, Laetitia Chapel, Nicolas Courty +3
While large volumes of unlabeled data are usually available, associated labels are often scarce. The unsupervised domain adaptation problem aims at exploiting labels from a source…
Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
Iris de Gélis, Sudipan Saha, Muhammad Shahzad +3
Change detection from traditional \added{2D} optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud \ad…
Change detection needs change information: improving deep 3D point cloud change detection
Iris de Gélis, Thomas Corpetti, Sébastien Lefèvre
Change detection is an important task that rapidly identifies modified areas, particularly when multi-temporal data are concerned. In landscapes with a complex geometry (e.g., urba…
A deep neural network for multi-species fish detection using multiple acoustic cameras
Guglielmo Fernandez Garcia, François Martignac, Marie Nevoux +2
Underwater acoustic cameras are high potential devices for many applications in ecology, notably for fisheries management and monitoring. However how to extract such data into high…